A Framework for Health-informed RUL-constrained Optimal Power Flow with Li-ion Batteries

TL;DR

This paper introduces a health-informed optimal power flow framework that incorporates remaining useful life constraints of Li-ion batteries, aiming to optimize operation while prolonging battery lifespan in grid applications.

Contribution

It proposes a novel RUL-constrained optimization framework using a data-driven health indicator and box constraints for efficient battery management in power systems.

Findings

Effective characterization of feasible operation space for batteries.

Improved long-term battery lifespan through constrained operation.

Validated approach on IEEE 39-bus system.

Abstract

Battery energy storage systems are widely adopted in grid-connected applications to mitigate the impact of intermittent renewable generations and enhance power system resiliency. Degradation of the battery during its service time is one of the major concerns in the deployment that strongly affects the long-term lifetime. Apart from environmental factors, this intrinsic property of a battery depends on the daily operating conditions. Thus, optimally engaging the daily operation of the battery based on its current status in order to meet the required remaining useful life becomes a practical and demanding need. To address this issue, this paper proposes a health-informed RUL-constrained optimal power flow framework to characterize the corresponding optimal feasible operation space. The targeted service lifespan is achieved if the battery's working condition is confined within this feasible domain. Equivalent box constraints are then constructed for better computational efficiency in solving the optimization problem. In this framework, a Monte Carlo-based data-driven approach and a health indicator (HI) representing the battery's current states are introduced. The performance of the proposed method is illustrated with the IEEE 39-bus system.