[Paper Review] Chance-Constrained Generic Energy Storage Operations under Decision-Dependent Uncertainty

N. D. Qi, Pierre Pinson+3 more|arXiv (Cornell University)|Jan 17, 2022

Smart Grid Energy Management4 citations

TL;DR

This paper proposes a chance-constrained optimization framework for generic energy storage (GES) dispatch under decision-dependent uncertainty (DDU), where available state-of-charge bounds depend on incentive signals and user discomfort. It introduces two tractable solution methods—robust reformulation for incomplete distributions and an iterative algorithm for known distributions—demonstrating reduced penalty costs and improved dispatch reliability through data-driven parameter identification.

ABSTRACT

Compared with large-scale physical batteries, aggregated and coordinated generic energy storage (GES) resources provide low-cost, but uncertain, flexibility for power grid operations. While GES can be characterized by different types of uncertainty, the literature mostly focuses on decision-independent uncertainties (DIUs), such as exogenous stochastic disturbances caused by weather conditions. Instead, this manuscript focuses on newly-introduced decision-dependent uncertainties (DDUs) and considers an optimal GES dispatch that accounts for uncertain available state-of-charge (SoC) bounds that are affected by incentive signals and discomfort levels. To incorporate DDUs, we present a novel chance-constrained optimization (CCO) approach for the day-ahead economic dispatch of GES units. Two tractable methods are presented to solve the proposed CCO problem with DDUs: (i) a robust reformulation for general but incomplete distributions of DDUs, and (ii) an iterative algorithm for specific and known distributions of DDUs. Furthermore, reliability indices are introduced to verify the applicability of the proposed approach with respect to the reliability of the response of GES units. Simulation-based analysis shows that the proposed methods yield conservative, but credible, GES dispatch strategies and reduced penalty cost by incorporating DDUs in the constraints and leveraging data-driven parameter identification. This results in improved availability and performance of coordinated GES units.

Motivation & Objective

• To address the gap in energy storage optimization by modeling decision-dependent uncertainties (DDUs) in generic energy storage (GES) operations.
• To develop a chance-constrained optimization (CCO) framework that incorporates DDUs affecting state-of-charge (SoC) bounds due to incentive signals and discomfort levels.
• To provide tractable solution methods for CCO problems under DDUs, applicable to both incomplete and known probability distributions.
• To verify the reliability of GES response using introduced reliability indices.
• To improve GES dispatch performance and reduce penalty costs by integrating DDUs into operational constraints.

Proposed method

• Formulates a day-ahead economic dispatch problem for GES using chance-constrained optimization (CCO) to manage probabilistic constraints under DDUs.
• Introduces a robust reformulation technique to handle general but incomplete distributions of DDUs, ensuring constraint feasibility under uncertainty.
• Develops an iterative algorithm tailored for specific and known distributions of DDUs, enabling precise constraint handling through successive approximation.
• Incorporates data-driven parameter identification to calibrate DDU models based on real-world response patterns and incentive structures.
• Defines reliability indices to evaluate the credibility and performance of GES dispatch strategies under uncertainty.
• Uses simulation-based analysis to validate the proposed methods and assess their impact on cost reduction and operational reliability.

Experimental results

Research questions

• RQ1 How can decision-dependent uncertainties (DDUs) in generic energy storage (GES) operations be effectively modeled in a day-ahead dispatch framework?
• RQ2 What tractable optimization methods can be developed to solve chance-constrained problems under DDUs when distributions are incomplete or fully specified?
• RQ3 How do DDU-aware dispatch strategies compare to traditional decision-independent uncertainty models in terms of cost and reliability?
• RQ4 To what extent can data-driven parameter identification improve the accuracy and performance of GES dispatch under DDUs?
• RQ5 What reliability metrics can credibly assess the performance of GES units under decision-dependent uncertainty?

Key findings

• The proposed CCO framework with DDUs yields more conservative but credible dispatch strategies compared to models ignoring DDUs.
• The robust reformulation method ensures constraint feasibility under incomplete distributional information for DDUs, enhancing operational safety.
• The iterative algorithm achieves accurate solutions when DDUs follow known distributions, improving dispatch precision.
• Simulation results show a measurable reduction in penalty costs by incorporating DDUs into the optimization constraints.
• Reliability indices confirm improved response credibility and performance of coordinated GES units under the proposed approach.
• Data-driven parameter identification enhances model fidelity, leading to better alignment with real-world user response behavior.