Performance and Reliability Foresight/SRP/Bolster

This page hosts all detailed analysis performed for the Bolster battery energy storage system.

Historical Operation

Megapack Level

Figure 1. Use this figure to view operational data from any time period. The figure dynamically adjusts the displayed resolution depending on the number of days viewed.

Round Trip Efficiency (RTE)

For the Bolster system, RTE is being calculated in two ways:

1. Continuously, using cumulative charge and discharge energy data
2. Discretely, by focusing in on individual high-energy charge and discharge events

More details and results follow.

Cumulative RTE Calculations

Cumulative RTE calculations are derived by dividing the cumulative discharge energy profile by the cumulative charge energy profile. This provides a more practical view into energy losses associated with keeping the system operational, even when not in active use (charge or discharge). For this reason, this RTE calculation varies depending on the batteries use case. For example, a battery that is performing asset deferral (i.e. only discharges a few times per year) would generally have a lower cumulative RTE compared to an identical battery performing daily energy arbitrage. This is also why cumulative RTE is not well suited as a metric of comparison for different systems performing different duties.

Figure 2. AC Cumulative charge and discharge energy for all available data. This figure should be updated daily as new data is received.

Individual Event RTE Calculations

Calculating RTE from individual, dedicated reference performance cycles is a valuable technique for assessing and validating the system's specified round trip efficiency. A deep charge followed by an equally deep discharge at similar power levels can be used to calculate a systems RTE in a way that is typically more in line with the how vendors contractually specify round trip efficiencies. This method of RTE calculation provides results that are easier to compare across different system and technologies, though care must be taken to ensure an apt comparison.

Although the Bolster BESS is not yet performing regular reference performance test cycles, recent operation can provide a glimpse into its round trip efficiency (RTE). Because the system is performing repeated high energy charge & discharge half cycles it is possible to compare these events to get a sense of round trip efficiency. Note, special care must be taken when performing calculations as the half cycles often do not start and end at the same state of charge.

To arrive at these calculations the following steps are taken:

1. A "half cycle detection" algorithm is applied to all historical State of Charge (SoC) and AC Power data.
2. All identified charge and discharge half cycles are paired into groups
3. For each group, a common SoC boundary is identified and the half cycles are windowed on these boundaries to ensure that the same SoC ranges are being compared
4. The energy of each bounded half cycle is compared to calculate the RTE

Figure 3 shows the half cycles grouped together. Hover over each charge / discharge group to see the round trip efficiency calculation and SoC Range details.

Figure 3. A look at past charge and discharge half cycles which are grouped together to calculate RTE. This figure is not automatically updated and exists simply to demonstrate the half cycle grouping algorithm used to calculate RTE below. Please email Joe Thompson (jthompson@epri.com) to request a figure update.

Figure 4. The RTE events from Figure 3 shown vs time (left) and average ambient temperature (right). This figure should be updated daily as new data is received. Please email Joe Thompson (jthompson@epri.com) with any questions or to request a figure update.

Unavailability Analysis

Background

System availability, or lack of availability (i.e. unavailability), is a valuable metric for assessing reliability. Collecting sufficient data to calculate unavailability has proven to be challenging. One method for calculating unavailability that is used in this study is to:

1. Find all times where the system is unable to operate at its rated power capacity, i.e. times where the system is derated due to some endogenous factor.
2. Sum the total impact of these derate events. This is done by calculating the "power•time" for each period of under-performance where "power" is the magnitude of the derate and "time" is the duration of the derate. This results in a value with units of MW•h, not to be confused with the unit of MWh typically used when discussing energy.
3. Divide the total impact from the derate events by the maximum possible availability which is the rated power capacity times the number of hours of operation.

This results in an unavailability fraction, the reciprocal of which is the system's availability.

Equation 1. Unavailability calculation.

In most situations it is difficult to find data for the P_limitt variable for the equation above. Fortunately, the Bolster system provides "charge power limit" and "discharge power limit" data at the system and individual Megapack level. This data is used to create the the P_limit profile that is needed for equation 1.

For the following calculations P_rated is 25 MW at the system level and 0.735 MW at the Megapack level.

When calculating unavailability in this way for an energy storage system it is critical to consider how the system's state of charge impacts the reported charge and discharge power limits. For example, as the system approaches its maximum state of charge the charge power limit will naturally drop to zero. Special care was taken in these calculations to avoid penalizing the systems availability under these conditions.

Results

Figure 5 provides 3 different views of the resulting unavailability analysis at the system (black dashed line) and individual Megapack (colored lines) levels.

Interpretations of these subplots are as follows:

1. Lifetime unavailability fraction: Each data point provides a retrospective view of unavailability on that day. The most recent headline unavailability is represented by the latest data points in this figure
2. Instantaneous unavailability fraction: Each data point represents that day's unavailability fraction at the system and Megapack levels.
3. Unit contribution fraction: Similar to the above figure, but the Megapack unavailability events are displayed in a way that shows their contribution to the overall system unavailability.

Figure 5. Unavailability analysis at the system and individual Megapack level.