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CapOptix: An Options-Framework for Capacity Market Pricing

Millend Roy, Agostino Capponi, Vladimir Pyltsov, Yinbo Hu, Vijay Modi

TL;DR

CapOptix introduces a risk-sensitive capacity pricing framework that treats capacity commitments as reliability options to better align investment incentives with system reliability in markets with high renewable penetration. It integrates CVaR-based reliability metrics, NetCONE break-even timing, and a versatile MRSM-driven price dynamics engine to price a strip of options representing capacity. Across multiple regional datasets (Germany, NYISO, CAISO, ERCOT, Italy), the framework demonstrates how reliability-option premia can recover investment costs while mitigating excessive over-procurement seen in traditional capacity auctions. The approach yields economically meaningful premia and contract durations, offering regulators and system operators a transparent tool to hedge scarcity risk without overpaying consumers.

Abstract

Electricity markets are under increasing pressure to maintain reliability amidst rising renewable penetration, demand variability, and occasional price shocks. Traditional capacity market designs often fall short in addressing this by relying on expected-value metrics of energy unserved, which overlook risk exposure in such systems. In this work, we present CapOptix, a capacity pricing framework that interprets capacity commitments as reliability options, i.e., financial derivatives of wholesale electricity prices. CapOptix characterizes the capacity premia charged by accounting for structural price shifts modeled by the Markov Regime Switching Process. We apply the framework to historical price data from multiple electricity markets and compare the resulting premium ranges with existing capacity remuneration mechanisms.

CapOptix: An Options-Framework for Capacity Market Pricing

TL;DR

CapOptix introduces a risk-sensitive capacity pricing framework that treats capacity commitments as reliability options to better align investment incentives with system reliability in markets with high renewable penetration. It integrates CVaR-based reliability metrics, NetCONE break-even timing, and a versatile MRSM-driven price dynamics engine to price a strip of options representing capacity. Across multiple regional datasets (Germany, NYISO, CAISO, ERCOT, Italy), the framework demonstrates how reliability-option premia can recover investment costs while mitigating excessive over-procurement seen in traditional capacity auctions. The approach yields economically meaningful premia and contract durations, offering regulators and system operators a transparent tool to hedge scarcity risk without overpaying consumers.

Abstract

Electricity markets are under increasing pressure to maintain reliability amidst rising renewable penetration, demand variability, and occasional price shocks. Traditional capacity market designs often fall short in addressing this by relying on expected-value metrics of energy unserved, which overlook risk exposure in such systems. In this work, we present CapOptix, a capacity pricing framework that interprets capacity commitments as reliability options, i.e., financial derivatives of wholesale electricity prices. CapOptix characterizes the capacity premia charged by accounting for structural price shifts modeled by the Markov Regime Switching Process. We apply the framework to historical price data from multiple electricity markets and compare the resulting premium ranges with existing capacity remuneration mechanisms.

Paper Structure

This paper contains 31 sections, 9 theorems, 34 equations, 4 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Modeling Assumptions and Specifications
  3. Contract Duration for new Capacities
  4. Reliability Metrics and Strike Price
  5. Conditional Value-at-Risk (CVaR)
  6. Reliability Options align with CVaR of Shortfall
  7. Reliability Options as Capacity Auctions
  8. Modeling Prices as Stochastic Processes and Premium as Option Derivative
  9. Distribution Comparison Metrics
  10. Markov Regime Switching Model (MRSM)
  11. Experiments and Results
  12. Case Study: Capacity Premium in Germany
  13. Rolling-window features and K-Means Clustering.
  14. Markov Auto-Regession.
  15. OU Process Estimation for each regime.
  16. ...and 16 more sections

Key Result

Proposition 1

Let $\{S_t\}_{t \geq 0}$ denote the stochastic process representing the wholesale electricity spot price. Suppose a capacity contract provides a fixed capacity $Q$ over the delivery window $[T, T+\tau]$, where $T$ is the infrastructure maturity time and $\tau$ is the delivery period. The strike pric where $[\cdot]^+ = \max(\cdot,0)$, $r$ is the risk-free discount and $\Delta t$ is the sample time

Figures (4)

  • Figure 1: Electricity prices across NYISO, and Germany.
  • Figure 2: MRSM regimes and contract-duration with $CapEx = 1{,}290{,}806$ €/MW, and $O\&M = 52$k €/MW-yr.
  • Figure 3: Sensitivity Analysis of Premium with respect to $\tau$, by varying $T$, $r$ and $K$.
  • Figure 4: Sensitivity Analysis of Premium with respect to $K$, by varying $T$, $r$ and $\tau$.

Theorems & Definitions (24)

  • Proposition 1: Capacity Premium as a Strip of European Options
  • Definition 1: NetCONE
  • Proposition 2
  • Definition 2: Conditional Value-at-Risk (CVaR)
  • Proposition 3: CVaR representation
  • Lemma 1: RO premium as price–tail CVaR
  • Definition 3: Marginal value of lost load (VOLL)
  • Theorem 1: Alignment of RO premium with CVaR of shortfall
  • Definition 4: CA Payment Stream
  • Definition 5: RO Payment Stream
  • ...and 14 more