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

TL;DR

This paper presents a robust microgrid dispatch strategy that balances battery degradation and operational costs by modeling temperature-dependent degradation and endogenous uncertainty, using a probabilistic approach and model predictive control.

Contribution

It introduces a novel probabilistic degradation model and a parametric MPC framework that jointly optimize dispatch decisions considering battery life-cycle and temperature effects.

Findings

The probabilistic degradation model accurately predicts temperature-dependent battery degradation.

The MPC framework effectively balances operational costs and battery lifespan.

Case studies demonstrate improved robustness and cost savings.

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.