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Robust Parametric Microgrid Dispatch Under Endogenous Uncertainty of Operation- and Temperature-Dependent Battery Degradation

Rui Xie, Jun Wang, Jiaxu Duan, Chao Ma, Yunhui Liu, Yue Chen

Abstract

Batteries play a critical role in microgrid energy management by ensuring power balance, enhancing renewable utilization, and reducing operational costs. However, battery degradation poses a significant challenge, particularly under extreme temperatures. This paper investigates the optimal trade-off between battery degradation and operational costs in microgrid dispatch to find a robust cost-effective strategy from a full life-cycle perspective. A key challenge arises from the endogenous uncertainty (or decision-dependent uncertainty, DDU) of battery degradation: Dispatch decisions influence the probability distribution of battery degradation, while in turn degradation changes battery operation model and thus affects dispatch. In this paper, we first develop an XGBoost-based probabilistic degradation model trained on experimental data across varying temperature conditions. We then formulate a parametric model predictive control (MPC) framework for microgrid dispatch, where the weight parameters of the battery degradation penalty terms are tuned through long-term simulation of degradation and dispatch interactions. Case studies validate the effectiveness of the proposed approach.

Robust Parametric Microgrid Dispatch Under Endogenous Uncertainty of Operation- and Temperature-Dependent Battery Degradation

Abstract

Batteries play a critical role in microgrid energy management by ensuring power balance, enhancing renewable utilization, and reducing operational costs. However, battery degradation poses a significant challenge, particularly under extreme temperatures. This paper investigates the optimal trade-off between battery degradation and operational costs in microgrid dispatch to find a robust cost-effective strategy from a full life-cycle perspective. A key challenge arises from the endogenous uncertainty (or decision-dependent uncertainty, DDU) of battery degradation: Dispatch decisions influence the probability distribution of battery degradation, while in turn degradation changes battery operation model and thus affects dispatch. In this paper, we first develop an XGBoost-based probabilistic degradation model trained on experimental data across varying temperature conditions. We then formulate a parametric model predictive control (MPC) framework for microgrid dispatch, where the weight parameters of the battery degradation penalty terms are tuned through long-term simulation of degradation and dispatch interactions. Case studies validate the effectiveness of the proposed approach.
Paper Structure (19 sections, 10 equations, 5 figures, 4 tables, 1 algorithm)

This paper contains 19 sections, 10 equations, 5 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Probabilistic Battery Degradation Model Using Experimental Data
  3. Battery Degradation Data Processing
  4. XGBoost-Based Probabilistic Battery Degradation Model
  5. Optimal Microgrid Dispatch Considering Battery Degradation
  6. PV-ESS Integrated Microgrid
  7. Parametric Microgrid Dispatch Considering Battery Degradation
  8. Robust Parameter Optimization
  9. Solution Method
  10. Case Study
  11. Settings
  12. Results of Battery Degradation Model
  13. Prediction Performance
  14. Analysis of Degradation Stress Factors
  15. Results of Microgrid Dispatch
  16. ...and 4 more sections

Figures (5)

  • Figure 1: Schematic of the PV-ESS integrated microgrid and power flow definitions.
  • Figure 2: Prediction intervals for samples in the test dataset, sorted according to the actual values.
  • Figure 3: Distribution of the median prediction error in the test dataset.
  • Figure 4: Prediction intervals for capacity degradation rate under different settings.
  • Figure 5: Battery capacity degradation curves of different methods. The solid lines represent the 90% worst-case degradation path, while the dashed lines show 10 Monte Carlo simulated paths under the same policy.