On the Role of Risk Aversion and Market Design in Capacity Expansion Planning.

• Author: Fraunholz, Christoph

1. INTRODUCTION

   In competitive power markets, investment decisions are based upon thorough profitability assessments. Thereby, investors typically show a high degree of risk aversion due to the capital intensity of large-scale generation and storage facilities, and the corresponding long-term investment horizons (Vazquez et al., 2002). The significant increase of renewable electricity generation in countries around the world further exacerbates the situation. Even under very high shares of renewables, a certain amount of dispatchable capacity will still be required to compensate for the intermittency of solar and wind power. Yet, the small number of (expected) operating hours as well as price volatility increase the risk of investments in the required firm capacity.

   Against this background, capacity remuneration mechanisms (CRMs) have been implemented in several regions of the world as an extension to an energy-only market (EOM), in which capacity providers are solely compensated for the amount of electricity they sell on the markets (Bublitz et al., 2019). CRMs aim to reduce the risks for new investments by offering capacity providers supplementary income on top of the earnings from selling electricity on the market. The additional firm capacity is then expected to help improve resource adequacy, i.e., avoid shortage situations.

   Nomenclature

   Abbreviations CCGT combined cycle gas turbine CCS carbon capture and storage CRM capacity remuneration mechanism CVaR conditional value at risk ECDF empirical cumulative distribution function ENS energy not served EOM energy-only market O&M operation and maintenance OCGT open cycle gas turbine TYNDP ten-year network development plan VaR value at risk Parameters [alpha] confidence level [lambda] degree of risk aversion Sets and Indices h hour i, [w.sub.i] weather year j technology option m market area s scenario y year Variables [??] endogenous electricity price forecast [??] expected profitability [pi] scenario-specific profitability [pi]* risk-adjusted profitability j* most profitable technology option P cumulative probability [rho] probability These developments illustrate that the interdependencies between investors' risk aversion and market design are crucial when analyzing transformation pathways of electricity systems. However, existing capacity expansion planning (1) models do not cover all aspects relevant for a realistic representation of real-world electricity markets, which are amongst others characterized by heterogeneous risk-averse actors and--particularly in the European case--cross-border effects of asymmetrical market design implementations.

   In our article, we therefore extend the agent-based electricity market model PowerACE to account for long-term uncertainties, such that capacity expansion planning can be carried out from an agent perspective and with diversified risk preferences. For this purpose, we construct model-endogenous scenario trees and implement a new decision metric that comprises the expected profitability and the corresponding conditional value at risk (CVaR) of a potential investment. The enhanced model is then applied in a case study covering multiple interconnected market areas with diverging market designs. This allows us to quantify the impact of risk aversion on capacity expansion, wholesale electricity prices, and resource adequacy for both, a European EOM design as well as asymmetrical CRM implementations.

   To the best of our knowledge, the developed capacity expansion planning approach is the first in the literature to consider the following key characteristics of real-world electricity markets in an integrated fashion:

   * deregulated market structure with heterogeneous and risk-averse actors,

   * path dependencies and lock-in effects arising from long investment horizons,

   * asymmetrical market designs with corresponding cross-border effects.

   The remainder of the article is structured as follows. In Section 2, we briefly review the relevant existing literature and outline how it is complemented by our analysis. Section 3 introduces the applied simulation model as well as all relevant extensions carried out for this article. We then describe the data and major assumptions of our case study in Section 4. The subsequent Section 5 presents and discusses the results of our simulations. In Section 6, we critically reflect limitations of our work. Finally, Section 7 concludes and derives policy implications of our analysis.

2. LITERATURE REVIEW AND RESEARCH GAP

   Capacity expansion planning is one of the traditional problems in electricity system design which is reflected by the several review papers available in the literature (e.g., Sadeghi et al., 2017; Koltsaklis and Dagoumas, 2018; Babatunde et al., 2019). In the following, we first summarize previous work on the role of risk aversion in capacity expansion planning. We then define several requirements for models that aim to represent real-world electricity markets in a realistic fashion. Based on this, we outline in what sense existing approaches fail to meet these criteria and how our work therefore complements the literature.

   Originally, optimization models from the perspective of a central planner that aims to maximize social welfare by minimizing total system cost were mostly applied for capacity expansion planning. Over time, this model class was extended to consider uncertainties (e.g., Swider and Weber, 2007; Spiecker et al., 2013; Fursch et al., 2014; Scott et al., 2021) and even risk aversion (e.g., Diaz et al., 2019; Mobius et al., 2021). However, such optimization models are not able to adequately represent competitive electricity markets, where investment decisions are made by individual market players based on market price expectations under imperfect information (Weber et al., 2021; Anwar et al., 2022).

   For this reason, equilibrium models have recently gained popularity. In these models, the individual profit maximization problems faced by the different market players are simultaneously solved in order to find an equilibrium with no incentive for any of the actors to unilaterally deviate. Equilibrium models generally allow to represent uncertainties (e.g., Schroder et al., 2013) as well as risk aversion (e.g., Ehrenmann and Smeers, 2011; Fan et al., 2012; Mays et al., 2019; Aryani et al., 2021). However, this type of models is particularly challenging to solve, so typically only small-scale systems can be investigated (Anwar et al., 2022).

   The computational challenges of equilibrium models can be mitigated by moving to other model types, such as system dynamics (e.g., Petitet et al., 2017) or agent-based simulations (e.g., Botterud et al., 2007; Anwar et al., 2022). However, while the mentioned articles consider uncertainties and risk aversion, none of them features a geographical scope covering more than a single country.

   Existing research shows that both, the flexibility of an electricity system (Mobius et al., 2021) and the market design (Ehrenmann and Smeers, 2011; Petitet et al., 2017) may decide on how big a role risk aversion plays in capacity expansion planning. Thus, given the European Commission's goal of creating an Internal Electricity Market, cross-border effects between interconnected market areas are a major aspect to be considered in European electricity market models. Moreover, several European countries have recently opted to introduce CRMs (Bublitz et al., 2019), which may come along with substantial cross-border effects. Finally, when investigating electricity market designs, it is important to model system transformation pathways in order to account for path dependencies and lock-in effects arising from long investment horizons. Considering multiple investment decision periods is therefore preferable over using a limited number of milestone years as typical for optimization and equilibrium models.

   To the best of our knowledge, there exists no approach in the literature that fulfills all of these requirements (cf. Table 1). In this article, we therefore enhance an existing agent-based simulation model, which allows us to adequately represent a dynamic capacity expansion planning in a deregulated market structure with heterogeneous and risk-averse actors. We then apply our approach in a case study covering multiple interconnected market areas with diverging market designs.

3. METHODOLOGY

   In the following sections, we describe the methodological approach of this article. To start with, Section 3.1 introduces the applied electricity market simulation model PowerACE and provides some details on the previously developed algorithm for capacity expansion planning from the perspective of individual agents. We then focus on the extensions to the existing approach that are required in order to account for uncertainties and risk aversion. For this purpose, Section 3.2 describes how the considered long-term uncertainties are modeled, whereas Section 3.3 concentrates on the decision making of the agents under consideration of their risk aversion.

   3.1 Overview of the Simulation Model PowerACE

   The methodological basis for this work is the established PowerACE model, which has previously been applied for various long-term scenario analyses of the European electricity markets (e.g., Keles et al., 2016; Bublitz et al., 2017; Ringler et al., 2017; Fraunholz et al., 2021a,b; Zimmermann et al., 2021). The focus of PowerACE lies on the simulation of interconnected day-ahead markets and different CRMs, with the relevant market participants--e.g., utility companies, regulators, consumers--represented by agents. In particular, the modeled utility companies decide on the short-term dispatch of their conventional power plants and storage units as well as long-term capacity expansions. Ultimately, the development of the markets emerges from the simulated behavior of all agents. The simulation model is continuously enhanced with new features. A detailed description of...