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Degradation-Infused Energy Portfolio Allocation Framework: Risk-Averse Fair Storage Participation

Parikshit Pareek, L. P. Mohasha Isuru Sampath, Anshuman Singh, Lalit Goel, Hoay Beng Gooi, Hung Dinh Nguyen

Abstract

This work proposes a novel degradation-infused energy portfolio allocation (DI-EPA) framework for enabling the participation of battery energy storage systems in multi-service electricity markets. The proposed framework attempts to address the challenge of including the rainflow algorithm for cycle counting by directly developing a closed-form of marginal degradation as a function of dispatch decisions. Further, this closed-form degradation profile is embedded into an energy portfolio allocation (EPA) problem designed for making the optimal dispatch decisions for all the batteries together, in a shared economy manner. We term the entity taking these decisions as `facilitator' which works as a link between storage units and market operators. The proposed EPA formulation is quipped with a conditional-value-at-risk (CVaR)-based mechanism to bring risk-averseness against uncertainty in market prices. The proposed DI-EPA problem introduces fairness by dividing the profits into various units using the idea of marginal contribution. Simulation results regarding the accuracy of the closed-form of degradation, effectiveness of CVaR in handling uncertainty within the EPA problem, and fairness in the context of degradation awareness are discussed. Numerical results indicate that the DI-EPA framework improves the net profit of the storage units by considering the effect of degradation in optimal market participation.

Degradation-Infused Energy Portfolio Allocation Framework: Risk-Averse Fair Storage Participation

Abstract

This work proposes a novel degradation-infused energy portfolio allocation (DI-EPA) framework for enabling the participation of battery energy storage systems in multi-service electricity markets. The proposed framework attempts to address the challenge of including the rainflow algorithm for cycle counting by directly developing a closed-form of marginal degradation as a function of dispatch decisions. Further, this closed-form degradation profile is embedded into an energy portfolio allocation (EPA) problem designed for making the optimal dispatch decisions for all the batteries together, in a shared economy manner. We term the entity taking these decisions as `facilitator' which works as a link between storage units and market operators. The proposed EPA formulation is quipped with a conditional-value-at-risk (CVaR)-based mechanism to bring risk-averseness against uncertainty in market prices. The proposed DI-EPA problem introduces fairness by dividing the profits into various units using the idea of marginal contribution. Simulation results regarding the accuracy of the closed-form of degradation, effectiveness of CVaR in handling uncertainty within the EPA problem, and fairness in the context of degradation awareness are discussed. Numerical results indicate that the DI-EPA framework improves the net profit of the storage units by considering the effect of degradation in optimal market participation.

Paper Structure

This paper contains 16 sections, 19 equations, 7 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Facilitator Framework
  3. EPA Framework
  4. Constraints in the EPA Framework
  5. Fair profit-sharing mechanism
  6. D&D Profiling and COD Formulation
  7. Degradation Model and Dataset Generation
  8. GP-based DEF Learning
  9. EPA Framework with VaR/CVaR Co-Optimization
  10. Results and Discussion
  11. Multi-Market Participation
  12. Comparison with the Risk-Neutral Approach
  13. Sensitivity towards Confidence Level
  14. Sensitivity towards Number of Samples
  15. Effect of Considering COD over ESS Dispatch
  16. ...and 1 more sections

Figures (7)

  • Figure 1: Structure of the proposed Facilitator framework
  • Figure 2: The degradation estimation function learning mechanism along with the degradation model used.
  • Figure 3: Empirical and closed-form prediction of degradation as a function of two-hour dispatch vector.
  • Figure 4: Empirical cumulative distribution function of Error in degradation estimation for a 500-kWh battery for different lengths of dispatch vectors.
  • Figure 5: Energy and reserve prices
  • ...and 2 more figures

Theorems & Definitions (2)

  • Remark
  • Remark