(45b) Data-Driven Battery Sizing and Market Participation

Authors: 
Dowling, A. W., University of Notre Dame
Sorourifar, F., University of Wisconsin-Madison
Renteria, J. A., University of Wisconsin-Madison
Zavala, V. M., University of Wisconsin-Madison
High-profile utility-scale battery storage systems have been deployed during the past few years to improve the grid reliability, ease renewable integration, curtail emissions, and reduce overall systems cost [1]. The financial soundness of these investments has yet to be proven because energy storage economics heavily depends on electricity market dynamics, energy policies, and long-term performance degradation.

We propose a multiscale optimization framework to co-optimize battery system designs and electricity market participation [2]. This includes mathematical models for California market rules, battery physics, and operational constraints. The models involve multi-year investment horizons with 5-minutes timesteps (to capture fast fluctuations of real-time prices), giving rise to linear programs with up to tens of million variables and constraints. We use a high-performance computing framework to analyze over 1 trillion historical price data points from the California ISO market from year 2015 and address key questions that drive battery storage economics:

  1. Which market products (energy, ancillary services) and market timescales (day-ahead, real-time) offer the greatest revenue potential?
  2. How much does battery degradation hinder market revenue? What is the potential impact for new battery technology breakthroughs?
  3. What are the optimal sizing, battery replacement strategies, and locations for storage systems? What is the expected payback period for recent investments?

Our analysis shows that most market revenues come from the fastest timescales (real-time energy markets, regulation ancillary services), indicating a strong need for fast flexibility in the California grid. Surprisingly, we find that market participation strategies are an order of magnitude more important that degradation effects. Moreover, our analysis reveals that small energy to power ratios are optimal, suggesting that current battery systems are often oversized. This is because revenue collected from market participation is often limited by the power rating of batteries and not by the available storage capacity. We also find strong spatial variability of the revenues in the California grid: the 1% most lucrative nodes (locations) provide revenues that are 10 times larger than the mean. This indicates that battery placement is key. Finally, we demonstrate the complex relationship between battery placement, sizing, degradation, and market participation by analyzing the Tesla Powerpack battery system and recent installation near Chino, CA [1,3].

References:

[1] Cardwell and Krauss (2017). A Big Test for Big Batteries. New York Times. https://www.nytimes.com/2017/01/14/business/energy-environment/california-big-batteries-as-power-plants.html

[2] Dowling, Kumar, Zavala (2016). A Multi-Scale Optimization Framework for Electricity Market Participation. Applied Energy 190, pg. 147-164.

[3] https://www.tesla.com/powerpack