(467d) Power Grid-Informed Experiments for Battery Systems: Integrating Electricity Markets, Scheduling Models, and Degradation Data

The injection of wind and solar resources in the power grid continues to grow and is expected to account for 30% of all power generation in 2030 [1]. Due to the intermittent nature of these renewable sources, balancing supply/demand in real-time has become more challenging, ultimately resulting in more volatile electricity prices [2]. It is thus necessary to explore the design and deployment of technologies that can help facilitate balancing in a scalable manner. Battery energy storage systems (BESSs) tend to be designed and tested for the ability to hold long-term charge [3-5], but it is crucial also to understand how these devices can be used to participate in highly dynamic markets such as the real-time market and frequency regulation. A key factor that arises here is capturing degradation effects under different charge/discharge signals. While there is a plethora of BESS scheduling models that aim to capture these effects, such models tend to be either overly detailed and/or lack experimental data [6-10].

To address these modeling challenges, this work presents a framework that utilizes real electricity market data and BESS scheduling models to help obtain typical charge/discharge signals that maximize economic performance and use such signals to guide battery cycling experiments, with the goal of obtaining degradation information in the form of capacity loss. The proposed framework reveals the timescale of storage needed to maximize economic performance and provides valuable data on long-term battery experiments that are strategically designed to measure BESS voltage at C-rates and rest times that align with timescales of real electricity markets. This proposed scheduling model also leverages experimental data on loss of voltage to obtain realistic dynamic capacity constraints that account for system degradation and self-discharge as a result of market participation decisions. This allows us to quantify the lost economic value that results from battery degradation, which is critical to inform design and investment decisions.

A case study utilizing this framework was completed for a lab-scale lithium-ion (Li-ion) BESS using data for day-ahead (DAM) and real-time markets (RTM) for all seven independent system operators (ISOs) of the United States. Key results from the study are the following: (1) short-term arbitrage through the purchase/sale of power after a single market interval accounts for 80-90% of the optimal participation policy in all ISOs for both DAM and RTM; (2) battery C-rates and resting times associated with participation in different electricity markets resulted in dramatically different degradation rates, indicating that market participation strategies and timescales strongly affect the lifetime of a battery; and (3) inclusion of experimental degradation data in scheduling models reveals a 23% reduction of revenue. These results highlight the ability of our integrative framework to provide valuable insights for experimental researchers and electricity market operators.

References:

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