Battery Aging: The Overlooked Factor in BESS Trading Strategies

Battery aging goes beyond technical concerns, 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 maximize short-term gains while reducing long-term profitability.

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 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 round-trip 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 real-world trading. As a result, the impact of different strategies—such as day-ahead trading versus intraday market participation—remains unclear.

The Overlooked Cost of Aging: How Trading Strategies Matter

Trading strategies and operational conditions directly shape BESS aging. For instance, aggressive intraday 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 ancillary 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 make things more tangible we simulated different load profiles based on real-world market data. Specifically, we used the German day-ahead and intraday market price data and simulated results with TWAICE Simulation Models.

For our model, we 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 – 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%. Whereas 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.

Extending our 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 below 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, mainly cause the differences in SoH forecasts.

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 day-ahead two cycles per day scenario, additional cycles result in increased wear and tear, accelerating aging compared to 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 maximize 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, C-rate.  

• Maximize BESS 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 return on investment.  

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