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Risk-Based Capacity Accreditation of Resource-Colocated Large Loads in Capacity Markets

Siying Li, Lang Tong, Timothy D. Mount

TL;DR

The paper tackles resource adequacy challenges posed by rapidly growing large loads by introducing a risk-based capacity accreditation framework for resource-colocated portfolios. It develops a convex optimization approach to compute ELCC via reliability-loss minimization, enabling scenario-based evaluation of colocated resources under uncertainty. The methodology is extended to include network constraints and is demonstrated through a hydrogen production facility colocated with renewables, storage, and a fuel cell, where the optimization-based ELCC accreditation consistently exceeds heuristic methods. This framework enhances the accuracy of capacity market accounting for large colocated loads and provides a scalable method for integrating data centers and manufacturing facilities into reliability-based planning.

Abstract

We study capacity accreditation of resource-colocated large loads, defined as large demands such as data center and manufacturing loads colocated with behind-the-meter generation and storage resources, synchronously connected to the bulk power system, and capable of participating in the wholesale electricity market as an integrated unit. Because the qualified capacity of a resource portfolio is not equal to the sum of its individual resources' qualified capacities, we propose a novel risk-based capacity accreditation framework that evaluates the collective contribution to system reliability. Grounded in the effective load carrying capability (ELCC) metric, the proposed capacity accreditation employs a convex optimization engine that jointly dispatches colocated resources to minimize reliability risk. We apply the developed methodology to a hydrogen manufacturing facility with colocated renewable generation, storage, and fuel cell resources.

Risk-Based Capacity Accreditation of Resource-Colocated Large Loads in Capacity Markets

TL;DR

The paper tackles resource adequacy challenges posed by rapidly growing large loads by introducing a risk-based capacity accreditation framework for resource-colocated portfolios. It develops a convex optimization approach to compute ELCC via reliability-loss minimization, enabling scenario-based evaluation of colocated resources under uncertainty. The methodology is extended to include network constraints and is demonstrated through a hydrogen production facility colocated with renewables, storage, and a fuel cell, where the optimization-based ELCC accreditation consistently exceeds heuristic methods. This framework enhances the accuracy of capacity market accounting for large colocated loads and provides a scalable method for integrating data centers and manufacturing facilities into reliability-based planning.

Abstract

We study capacity accreditation of resource-colocated large loads, defined as large demands such as data center and manufacturing loads colocated with behind-the-meter generation and storage resources, synchronously connected to the bulk power system, and capable of participating in the wholesale electricity market as an integrated unit. Because the qualified capacity of a resource portfolio is not equal to the sum of its individual resources' qualified capacities, we propose a novel risk-based capacity accreditation framework that evaluates the collective contribution to system reliability. Grounded in the effective load carrying capability (ELCC) metric, the proposed capacity accreditation employs a convex optimization engine that jointly dispatches colocated resources to minimize reliability risk. We apply the developed methodology to a hydrogen manufacturing facility with colocated renewable generation, storage, and fuel cell resources.

Paper Structure

This paper contains 13 sections, 1 theorem, 9 equations, 3 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Summary of Contribution
  4. Resource Modeling and ELCC
  5. Resource Modeling
  6. ELCC for Colocated Resources
  7. Reliability Evaluation with Optimal Dispatch
  8. Reliability Loss Minimization
  9. Convex Reformuation of Reliability Loss Minimization
  10. Extension to Network Constraints
  11. Risk-based Capacity Accreditation
  12. ELCC of Resource-Colocated Hydrogen
  13. Conclusions

Key Result

Proposition 1

If the objective function $R(\cdot)$ is convex, e.g., as in eq:eue_objective for EUE minimization, then the reliability optimization problem eq:opt_reliability can be equivalently reformulated as the following convex program: The aggregate actual power consumption of flexible-demand resources, denoted by $\mathbf{1}^\top\tilde{\boldsymbol{P}}^{\hbox{\tiny\sf FLEX}}_t$, is given by where $S_t\!

Figures (3)

  • Figure 1: Hydrogen production colocated with renewable, storage and fuel cell.
  • Figure 2: Two toy examples of colocated wind-storage ELCC. Upper row: sub-additive case. Lower row: super-additive case.
  • Figure 3: ELCC versus installed capacity for the colocated resource under different EUE targets.

Theorems & Definitions (2)

  • Proposition 1
  • proof : Sketch of Proof