Participation in wholesale energy markets offers a critical source of revenue for battery energy storage systems (BESS). Across transmission organizations in the U.S., storage operators can deploy their units to participate in various wholesale markets.
In Texas and California, where grid operators Electric Reliability Council of Texas (ERCOT) and California Independent System Operator (CAISO) have established favorable conditions for storage market participation, companies engage in the day-ahead markets, with storage playing a role in energy and certain ancillary services. For those willing to take on greater risk in pursuit of higher rewards (especially in ERCOT’s more open market structure), storage operators can also participate in real-time markets.
Those revenue opportunities are limited, though, if energy storage systems are not performing to their full potential. As operations and maintenance (O&M) personnel can attest, battery aging is one of the main culprits in the degradation of a BESS. It is a natural part of the lithium-ion battery lifecycle, but it is not a linear one—several factors shape how batteries age and ultimately how this impacts performance. For those maintaining a BESS, it is critical to understand how to optimize battery aging so the BESS can still participate in energy markets with the most revenue generation possible.
Without the right tools, it can be challenging to understand battery aging. This is where battery simulation models come into play. They optimize operational strategies and profitability of BESS by providing precise aging insights and increasing transparency on trading decisions.
Understanding Battery Aging: Common Challenges
Battery aging goes beyond technical concern; it is a strategic challenge that directly impacts the profitability of a BESS. Every charge and discharge decision affects its lifespan, and without a clear understanding of aging costs, asset owners and operators risk making suboptimal decisions such as maximizing short-term gains while reducing long-term profitability.
Battery aging is an unavoidable process affected by multiple factors, including the depth and frequency of charge-discharge cycles or operating temperatures. These and other factors contribute to the gradual decline in a BESS’s capacity and roundtrip efficiency over time. Therefore, considering battery aging in BESS operations and trading decisions is essential. Without clear insights into how operational decisions affect aging, operators face several key challenges:
■ Dependence on Supplier Aging Curves. Supplier aging curves often present overly cautious estimates, potentially leading to inaccurate aging projections and, consequently, flawed revenue forecasts, which can create overly pessimistic financial models and missed revenue opportunities.
■ Balancing Revenue and Aging Impact. Limited transparency on how trading decisions impact aging can lead to suboptimal decisions, such as over-cycling BESS for short-term revenue gains at the cost of higher long-term aging expenses.
■ Limited Adaptability to Trading Strategies. Most supplier-provided aging curves only cover one or two predefined scenarios over a battery’s lifespan, failing to account for the diverse conditions of energy market participation. As a result, the impact of different strategies—such as day-ahead trading versus real-time markets—remains unclear.
The Overlooked Cost of Aging: How Trading Strategies Matter
Energy market strategies and operational conditions directly shape BESS aging. For instance, aggressive real-time trading strategies usually capitalize on frequent price arbitrage opportunities, requiring high cycling. High cycling increases charge-discharge stress and leads to faster aging. In contrast, longer-duration energy shifting strategies, such as peak shaving or day-ahead trading, may involve fewer but deeper cycles. Additionally, participation in services like frequency regulation often involves rapid but shallow cycling. All these different operational strategies create different load profiles on BESS and therefore lead to different aging patterns over time.
To provide an example of the volatility of trading strategies, researchers at TWAICE simulated different load profiles based on real-world market data. Specifically, the researchers used the German day-ahead and intraday market price data, and simulated results with TWAICE simulation models. Germany and the U.S. have a comparable approach to electricity market operations, both utilizing market-based mechanisms for energy trading, balancing, and grid stability. Therefore, the findings detailed here can also be applied to the day-ahead and real-time markets of operators like ERCOT and CAISO.
For this model, researchers considered three different BESS operational strategies under varying conditions: one cycle per day in day-ahead market, two cycles per day in day-ahead market, and a combination of day-ahead and intraday market trading.
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1. Exemplary aging forecasts of different battery energy storage system (BESS) operating conditions—12 weeks (SoH = state of health). Courtesy: TWAICE |
Figure 1 illustrates how these operations lead to different state of health (SoH) results over 12 weeks. Operating a BESS two cycles per day in the day-ahead market leads to a SoH of about 91%; the one cycle day-ahead or the combination of day-ahead and intraday market trading leads to less aging with SoH values of about 92.5%. These differences might seem small, but they make a significant difference when looking at the entire life of a BESS.
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2. Exemplary aging forecasts of different BESS operating conditions—10 years. Courtesy: TWAICE |
Extending the simulation to a 10-year period, however, reveals significant differences in SoH trajectories among these three operating strategies, as shown in Figure 2. Operating a BESS two cycles per day in the day-ahead market for 10 years would result in a SoH less than 80%. In contrast, combining day-ahead and intraday trading over 10 years would result in a far healthier system with a SoH above 90%. In short, the choice of operating strategies directly impacts the longevity of your BESS.
As explained above, differences in SoH forecasts for BESS are caused primarily by variations in operational load profiles, including factors such as depth of discharge (DoD), state of charge (SoC) levels at rest, and cycling frequency.
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3. Example day for load profiles of different BESS operating conditions (SoC = state of charge). Courtesy: TWAICE |
Continuing with the same example, Figure 3 shows load profiles for an example day for the three different trading scenarios. Comparing the day-ahead scenarios, the one-cycle-per-day scenario has lower cycling frequency than the two-cycle-per-day scenario. This helps to mitigate stress on the BESS and leads to slower aging and a longer lifespan. On the other hand, in the day-ahead two-cycles-per-day scenario, additional cycles result in increased wear and tear, accelerating aging compared to a one-cycle-per-day scenario. However, it may also generate higher short-term revenue gains.
In the third scenario with intraday participation, the BESS operates more dynamically, reacting to short-term price fluctuations during the day. The load profile for an example day results in frequent but shallower charge-discharge cycles. This results in the BESS operating at lower average SoC levels, which helps reduce calendar-aging effects and can lead to the more favorable long-term SoH trajectory shown in Figure 2.
These SoH variations demonstrate that trading decisions and their distinct load profiles significantly influence battery aging. For asset owners and operators, recognizing how operating factors like cycling patterns affect battery aging is essential to making informed decisions and maximizing BESS profitability over the lifetime.
A Data-Driven Approach to Optimizing Operational Strategies
Battery simulation models provide asset owners and operators with a data-driven approach to optimizing BESS operations. By offering clear insights into battery aging, these models enable smart decision-making for maximized BESS profitability. With the ability to simulate real-world scenarios, operators can proactively test and refine their trading strategies. Battery simulation models can help BESS owners and operators to:
■ Accurately Predict Aging Costs. Operators can quantify how different operational strategies impact battery aging over time. It helps them to assess the true cost of cycling.
■ Better Understand BESS Aging Dynamics. Operators can simulate different operational load profiles tailored to real-world scenarios and test various stress factors such as DoD, temperature, SoC window, and charge/discharge rate (C-rate).
■ Maximize BESS Return on Investment (ROI) with Informed Decisions. By using data-driven battery-aging insights to refine operational strategies, owners and operators can improve both short-term financial gains and long-term ROI.
In applying simulation models, storage operators can determine which market participation strategies make the most sense for their BESS. If the operator is focused on high short-term revenue, the simulation software will display how this could be detrimental to long-term revenue, while the software will also predict how a less taxing operational strategy could ultimately realize more revenue.
Battery aging is an unavoidable fact of BESS operations. With the right tools at hand, though, O&M personnel can help optimize market participation strategies to ensure that aging is not an unplanned limitation to BESS lifetime ROI.
—Markus Mühlbauer is lead battery modeling engineer at TWAICE, and a seasoned expert in battery analytics and simulation. Ece Aras is associate product manager at TWAICE, where she is responsible for simulation solutions.
