Dynamic Valuation of Battery Lifetime

TL;DR

This paper introduces a dynamic valuation framework for battery lifetime that considers internal degradation and market scenarios, demonstrating its effectiveness through case studies on arbitrage and regulation.

Contribution

It presents a novel dynamic programming-based approach with a piecewise linear approximation for long-term battery valuation in grid applications.

Findings

Battery lifetime value depends on market environment and health state.

Second-life batteries can yield over 50% of new battery value.

Frequency regulation offers twice the revenue of price arbitrage.

Abstract

This paper proposes a dynamic valuation framework to determine the opportunity value of battery capacity degradation in grid applications based on the internal degradation mechanism and utilization scenarios. The proposed framework follows a dynamic programming approach and includes a piecewise linear value function approximation solution that solves the optimization problem over a long planning horizon. The paper provides two case studies on price arbitrage and frequency regulation using real market and system data to demonstrate the broad applicability of the proposed framework. Results show that the battery lifetime value is critically dependent on both the external market environment and its internal state of health. On the grid service side, results show that second-life batteries can provide more than 50% of the value compared to new batteries, and frequency regulation provides two times more revenue than price arbitrage throughout the battery lifetime.