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Reformulating Energy Storage Capacity Accreditation Problem with Marginal Reliability Impact

Qian Zhang, Feng Zhao, Tongxin Zheng, Le Xie

TL;DR

This work tackles the challenge of credibly crediting energy storage in capacity markets by introducing Marginal Reliability Impact (MRI) as the key metric. It reformulates the reliability dispatch model as an optimization to compute MRI directly from Lagrange multipliers, proving that the resulting EUE is piecewise-linear in QC and that MRI is non-negative, with a clear path to computing QMRIC = QC × rMRI where $rMRI = MRI / MRI_{perfect}$. The approach is validated on a modified California system, revealing how storage duration and dispatch rules influence MRI and informing policymakers on reliability criteria (LOLE, LOLH, EUE) and market design. Collectively, the results provide a scalable, theory-grounded framework for MRI-based storage accreditation, with practical guidance for system operators and regulators amid increasing renewable and storage penetration.

Abstract

To enhance the efficiency of capacity markets, many electricity markets in the U.S. are adopting or planning to implement marginal capacity accreditation reforms. This paper provides new insights into energy storage capacity accreditation using Marginal Reliability Impact (MRI). We reformulate the commonly used reliability-based storage dispatch model as an optimization problem, enabling direct calculation of the MRI from the Lagrange multipliers, rather than using brute-force perturbation analysis. The analysis demonstrates that the EUE is a piecewise linear function and the storage MRI retains a non-negative property across various system scenarios. We further explore the influence of qualified capacity (QC), storage dispatch rules, and other key factors on storage accreditation, providing practical insights for system operators. Additionally, comparisons of storage capacity accreditation under different reliability criteria offer valuable guidance for policymakers in setting future standards. Numerical results from a modified California system validate our findings and highlight several important phenomena associated with the MRI-based accreditation scheme.

Reformulating Energy Storage Capacity Accreditation Problem with Marginal Reliability Impact

TL;DR

This work tackles the challenge of credibly crediting energy storage in capacity markets by introducing Marginal Reliability Impact (MRI) as the key metric. It reformulates the reliability dispatch model as an optimization to compute MRI directly from Lagrange multipliers, proving that the resulting EUE is piecewise-linear in QC and that MRI is non-negative, with a clear path to computing QMRIC = QC × rMRI where . The approach is validated on a modified California system, revealing how storage duration and dispatch rules influence MRI and informing policymakers on reliability criteria (LOLE, LOLH, EUE) and market design. Collectively, the results provide a scalable, theory-grounded framework for MRI-based storage accreditation, with practical guidance for system operators and regulators amid increasing renewable and storage penetration.

Abstract

To enhance the efficiency of capacity markets, many electricity markets in the U.S. are adopting or planning to implement marginal capacity accreditation reforms. This paper provides new insights into energy storage capacity accreditation using Marginal Reliability Impact (MRI). We reformulate the commonly used reliability-based storage dispatch model as an optimization problem, enabling direct calculation of the MRI from the Lagrange multipliers, rather than using brute-force perturbation analysis. The analysis demonstrates that the EUE is a piecewise linear function and the storage MRI retains a non-negative property across various system scenarios. We further explore the influence of qualified capacity (QC), storage dispatch rules, and other key factors on storage accreditation, providing practical insights for system operators. Additionally, comparisons of storage capacity accreditation under different reliability criteria offer valuable guidance for policymakers in setting future standards. Numerical results from a modified California system validate our findings and highlight several important phenomena associated with the MRI-based accreditation scheme.
Paper Structure (21 sections, 5 theorems, 27 equations, 5 figures, 5 tables, 1 algorithm)

This paper contains 21 sections, 5 theorems, 27 equations, 5 figures, 5 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Marginal Reliability Impact
  3. MRI as a Rectifier for Qualified Capacity
  4. The Role of MRI in Forward Capacity Market
  5. Energy Storage Dispatch Model
  6. Overview of Energy Storage Dispatch Assumption
  7. Reliability Dispatch
  8. MRI-based Storage Capacity Accreditation
  9. Preliminary
  10. Single Energy Storage Resource
  11. Multiple Energy Storage Resources
  12. Discussion
  13. Qualified Capacity
  14. Initial SoC
  15. Round-trip Efficiency
  16. ...and 6 more sections

Key Result

Theorem 1

If the QC is defined as either storage energy capacity $\overline{S}$ or power capacity $\overline{x}$ (or any positive linear combination thereof), the MRI of energy storage is non-negative, and the EUE function is piecewise linear (both with respect to QC), regardless of system condition.

Figures (5)

  • Figure 1: The Calculation Process of Reliability Metrics and MRI
  • Figure 2: The system net capacity surplus without considering energy storage (August)
  • Figure 3: The value of EUE under the perturbation of different power and energy capacities
  • Figure 4: System reliability under different metrics and ICR levels
  • Figure 5: The storage rMRI under different ICR levels

Theorems & Definitions (12)

  • Definition 1: Marginal Reliability Impact fengmri
  • Definition 2: relative Marginal Reliability Impact fengmri
  • Definition 3: Net Capacity Surplus
  • Definition 4: Net Power Surplus and Deficiency
  • Theorem 1: Non-negative MRI and Piecewise linear EUE: Single Energy Storage
  • proof : Proof
  • Corollary 1: Direct Calculation of MRI: Single Energy Storage
  • Theorem 2: Non-negative MRI and Piecewise linear EUE: Multiple Energy Storage
  • proof
  • Corollary 2: Direct Calculation of MRI: Multi Energy Storage
  • ...and 2 more