Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Capacity Credit Evaluation of Generalized Energy Storage Considering Strategic Capacity Withholding and Decision-Dependent Uncertainty

Ning Qi, Pierre Pinson, Mads R. Almassalkhi, Yingrui Zhuang, Yifan Su, Feng Liu

TL;DR

This work develops a capacity credit evaluation framework for generalized energy storage that explicitly accounts for cross-market strategic withholding and long-term decision-dependent uncertainty. It introduces a market-oriented, risk-averse coordinated dispatch and a data-driven distributionally robust chance-constrained optimization to mitigate DDU, along with a novel ESCS CC metric for inter-temporal planning. Numerical results on a modified IEEE RTS-79 system with 20 years of Elia data show that ignoring DDU can significantly misestimate CC and profits, while the proposed approach delivers robust CC estimates and improved economic performance, especially when storage with longer duration is employed. The findings underscore the importance of including market dynamics and DDU structure in capacity-market decision-making and demonstrate how ESCS can facilitate co-planning of transmission and storage resources in decarbonized power systems.

Abstract

This paper proposes a novel capacity credit evaluation framework to accurately quantify the contribution of generalized energy storage (GES) to resource adequacy, considering both strategic capacity withholding and decision-dependent uncertainty (DDU). To this end, we establish a market-oriented risk-averse coordinated dispatch method to capture the cross-market reliable operation of GES. The proposed method is sequentially implemented along with the Monte Carlo simulation process, coordinating the pre-dispatched price arbitrage and capacity withholding in the energy market with adequacy-oriented re-dispatch during capacity market calls. In addition to decision-independent uncertainties in operational states and baseline behavior, we explicitly address the inherent DDU of GES (i.e., the uncertainty of available discharge capacity affected by the incentives and accumulated discomfort) during the re-dispatch stage using the proposed data-driven distributional robust chance-constrained approach. Furthermore, a capacity credit metric called equivalent storage capacity substitution is introduced to quantify the equivalent deterministic storage capacity of uncertain GES. Simulations on the modified IEEE RTS-79 benchmark system with 20 years real-world data from Elia demonstrate that the proposed method yields accurate capacity credit and improved economic performance. We show that the capacity credit of GES increases with more strategic capacity withholding but decreases with more DDU levels. Key factors, such as capacity withholding and DDU structure impacting GES's capacity credit are analyzed with insights into capacity market decision-making.

Capacity Credit Evaluation of Generalized Energy Storage Considering Strategic Capacity Withholding and Decision-Dependent Uncertainty

TL;DR

This work develops a capacity credit evaluation framework for generalized energy storage that explicitly accounts for cross-market strategic withholding and long-term decision-dependent uncertainty. It introduces a market-oriented, risk-averse coordinated dispatch and a data-driven distributionally robust chance-constrained optimization to mitigate DDU, along with a novel ESCS CC metric for inter-temporal planning. Numerical results on a modified IEEE RTS-79 system with 20 years of Elia data show that ignoring DDU can significantly misestimate CC and profits, while the proposed approach delivers robust CC estimates and improved economic performance, especially when storage with longer duration is employed. The findings underscore the importance of including market dynamics and DDU structure in capacity-market decision-making and demonstrate how ESCS can facilitate co-planning of transmission and storage resources in decarbonized power systems.

Abstract

This paper proposes a novel capacity credit evaluation framework to accurately quantify the contribution of generalized energy storage (GES) to resource adequacy, considering both strategic capacity withholding and decision-dependent uncertainty (DDU). To this end, we establish a market-oriented risk-averse coordinated dispatch method to capture the cross-market reliable operation of GES. The proposed method is sequentially implemented along with the Monte Carlo simulation process, coordinating the pre-dispatched price arbitrage and capacity withholding in the energy market with adequacy-oriented re-dispatch during capacity market calls. In addition to decision-independent uncertainties in operational states and baseline behavior, we explicitly address the inherent DDU of GES (i.e., the uncertainty of available discharge capacity affected by the incentives and accumulated discomfort) during the re-dispatch stage using the proposed data-driven distributional robust chance-constrained approach. Furthermore, a capacity credit metric called equivalent storage capacity substitution is introduced to quantify the equivalent deterministic storage capacity of uncertain GES. Simulations on the modified IEEE RTS-79 benchmark system with 20 years real-world data from Elia demonstrate that the proposed method yields accurate capacity credit and improved economic performance. We show that the capacity credit of GES increases with more strategic capacity withholding but decreases with more DDU levels. Key factors, such as capacity withholding and DDU structure impacting GES's capacity credit are analyzed with insights into capacity market decision-making.
Paper Structure (27 sections, 1 theorem, 12 equations, 10 figures, 10 tables, 2 algorithms)

This paper contains 27 sections, 1 theorem, 12 equations, 10 figures, 10 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Background and motivation
  3. Literature review
  4. Research gap
  5. Contributions
  6. Paper Organization
  7. Market-Oriented Risk-Averse Coordinated Dispatch
  8. Motivation
  9. Problem Formulation
  10. Problem Reformulations and Solution Methodology
  11. Practical GES Performance Simulation
  12. CC Evaluation Framework of GES
  13. Numerical Studies
  14. Set-Up
  15. GES Operation and Adequacy Performance
  16. ...and 12 more sections

Key Result

Lemma 1

Let $\xi$ be a stochastic variable with a probability distribution in the family $\mathcal{P}(\mu^*\text{,} \sigma^{2*}\text{,} \underline{\xi}^*\text{,} \overline{\xi}^*)$, where the mean and variance are $\mu^*$ and $\sigma^{2*}$, and the support is $[\underline{\xi}^*\text{,} \overline{\xi}^*]$.

Figures (10)

  • Figure 1: Practical performance of GES in CAISO (a) strategic capacity withholding in energy market and (b) capacity availability in capacity market.
  • Figure 2: Diagram of market-oriented risk-averse coordinated dispatch for GES.
  • Figure 3: Comparision of SoC bounds and GES strategy under DIU and DDU.
  • Figure 4: Flowchart of CC evaluation of GES under DDU.
  • Figure 5: Convergence performance of SMCS under different dispatch methods: (a) EENS and (b) CoV.
  • ...and 5 more figures

Theorems & Definitions (4)

  • Remark 1: Pre-dispatch Strategy
  • Remark 2: Learning of DDU Structure
  • Remark 3: Re-dispatch Strategy
  • Lemma 1