Uncertain bidding zone configurations: The role of expectations for transmission and generation capacity expansion

M Ambrosius,J Egerer,V Grimm,A.H Van Der Weijde

doi:10.1016/j.ejor.2020.01.024

Abstract

Ongoing policy discussions on the reconfiguration of bidding zones in European electricity markets induce uncertainty about the future market design. This paper deals with the question of how this uncertainty affects market participants and their long-run investment decisions in generation and transmission capacity. Generalizing the literature on pro-active network expansion planning, we propose a stochastic multilevel model which incorporates generation capacity investment, network expansion, and market operation, taking into account uncertainty about the future bidding zone configuration. Using a stylized two-node network, we disentangle different effects that uncertainty has on market outcomes. If there is a possibility that future bidding zone configurations provide improved regional price signals, welfare gains materialize even if the change does not actually take place. As a consequence, welfare gains of an actual change of the bidding zone configuration are substantially lower due to those anticipatory effects. Additionally, we show substantial distributional effects in terms of both expected gains and risks, between producers and consumers and between different generation technologies.

Highlights

The need for decarbonization of the electricity system, together with structural changes in electricity demand and generation costs, is driving significant investment in new and upgraded electricity transmission and generation capacity
We model real power flows via a lossless direct current (DC) approximation (Schweppe et al 1988)
We evaluate the model for a discrete set of probabilities that market participants assume for the implementation of two bidding zones, i.e., P (k = 2) ∈ {0, 0.1, . . . , 1}

Summary

Introduction

The need for decarbonization of the electricity system, together with structural changes in electricity demand and generation costs, is driving significant investment in new and upgraded electricity transmission and generation capacity. Planning methods have evolved to explicitly consider how optimal investment decisions should be made under uncertainty These include stochastic optimization models, which seek to identify decisions that are optimal given the full range of possible future market conditions (for an overview, see Conejo et al 2010, Möst and Keles 2010, Oliveira and Costa 2018, and, more generally, Powell 2019), and robust optimization models, which optimize decisions such that the worst possible outcomes are still feasible (Ruiz and Conejo 2015).

Results

Discussion

Conclusion