Method for Analyzing the Value of Distributed Energy Storage at the Facility Level – Step 5: Accounting for Round Trip Efficiency

Figure 1 shows the current step in the evaluation methodology…

Figure 1 Showing the current step (step 5: accounting for round trip efficiency) in the methodology for evaluating a facility level energy storage deployment.

Because the round trip efficiencies of each storage device is less than 100%, more energy will be consumed during the charge phase than will be delivered during discharge. This will increase the overall energy consumed in a day. This increase in overall energy must be taken into account when determining the cost benefit of each energy storage system. The additional energy required, per day, is found using the following equation:

For the LightSail RAES V1 a round trip efficiency of 70% was used while a round trip efficiency of 85% was used for the VRB-ESS®. The additional energy costs resulting from the round-trip efficiency of the energy storage system were found using the following equation:

These additional, round trip efficiency related energy costs were subtracted from the demand charge management savings for each energy storage device to provide a more accurate estimate of overall savings. The extent to which the round trip efficiency reduces the demand charge savings depends heavily on the facility’s energy billing scheme. If peak energy costs are significantly greater than the off-peak energy costs (which is common for larger facilities) it may be possible that the demand charge savings reduction may be eliminated or reversed by the fact that inexpensive energy is being used to charge the device. Discharging the device during peak periods could, in this case, allow the device to also be profitably deployed in an arbitrage or Time Of Use (TOU) energy cost management application. The facility that housed the software company had a relatively unique billing scheme, for a facility of its size, because the same energy rate was paid during peak and off-peak periods. In that example the increased energy to overcome the round trip efficiency directly reduced the demand charge management savings.

Figure 2 showing the Load profile on peak usage day at the facility. The dashed line represents the peak that would be realized if the energy storage device were operated at full power.

Many modern energy storage devices require a charging time that is equal to or only slightly greater than the discharge interval. There are some devices which can actually charge faster than they discharge. In Figure 2, the time interval for the area below the dashed line and above the load curve represents the charging interval for the energy storage device. By visual inspection it is seen that the charging interval area is more than twice the discharge area. If the charging interval area were not clearly at least twice the discharge area it would be important to pay more attention to the duration of the charging interval. It would not be an effective energy storage device deployment if charge interval were so large that it impacted the devices ability to reduce the peak power. 

Method for Analyzing the Value of Distributed Energy Storage at the Facility Level – Step 4: Determine Reduced Peak - Further Explanation

I received a few questions related to step 4 from people asking for more information and an example of the method that I described for calculating the new reduced peak that can be realized by deploying an energy storage device in the demand charge management application. To do this I posted the following video on youtube:

The video references a spreadsheet that I created for this example. The spreadsheet contains hypothetical load profile information for one week and is used in the video to show how to determine the possible reduction in peak energy within the constraints of the energy storage device's power and energy rating. The spreadsheet can be downloaded from here.

One thing to note, is that the video shows a very manual process of running goal seek optimization in Microsoft Excel. This may not be practical for a large number of data points in your load profile. This very manual process can be accelerated by using a macro. I used the following macro during my first analysis of the software company:

Sub Macro3()

Worksheets("<sheet name>").Activate

For Each cll In Range("p6:p11")
cll.goalseek goal:=1000, ChangingCell:=cll.Offset(, -7)
Next cll

End Sub


Macro notes:

• p6:p11 - the range of cells that need to match the goal (i.e. these cells should = the goal by changing these cells)
• 1000 - the goal
• Offset (column , row) - offset from cells that get changed to make range of cells match the goal

Certainly there are many online resources to learn how to create a macro to run goal seek optimization on multiple cells. Here are a few that I found:

http://youtu.be/v6xcVpO2YDc

http://excel.kingofmath.com/?p=267

http://newtonexcelbach.wordpress.com/2009/07/25/using-goal-seek-on-multiple-cells/

Please post questions and comments.

Method for Analyzing the Value of Distributed Energy Storage at the Facility Level – Step 4: Determine Reduced Peak

Figure 1 shows the current step in the evaluation methodology…

Figure 1 Showing the current step in the methodology for evaluating a facility level energy storage deployment.

If the selected electrical energy storage devices (LightSail RAES V1 or the Prudent VRB-ESS® ) were to be deployed without onsite renewable energy generation, in this scenario, the device would only be used for the purpose of Demand Charge Management. In an optimal deployment, the facility would have a method of predicting the peak power consumption for each day. In this situation the energy storage device would be fully discharged in order to lower the peak power consumption for the day. The goal of this deployment would be lowering the highest peak for the month and reducing demand charge payments. Predicting the peak power can be done through reviewing historical load profile records, forecasting the weather to understand HVAC loads, managing/scheduling power consumption events, etc.(Baxter, 2012). For this first pass evaluation, only load profile records will be used.

Figure 2 shows the load profile for the facility on the peak power consumption day of the year at the facility. The dashed line in Figure 2 represents the new reduced peak that would be realized if either the LightSailRAES V1 or the Prudent VRB-ESS® were deployed. Conveniently, both devices have the same power and energy rating, 250 kW and 1000 kWh (depending on the amount of storage media) respectively. The equation for the new reduced peak at full power is:

Figure 2 showing the Load profile on peak usage day at the facility. The dashed line represents the peak that would be realized if the energy storage device were operated at full power.

Using a 250 kW energy storage device to reduce the 1,583 kW demand peak to a new shifted peak of 1,333 kW would require 1123 kWh of energy output from the device. In Figure 2 the area above the dashed line and below the power usage curve represents the energy produced by the energy storage device. As both devices are rated at 1 MWh, the power output of the device must be reduced from the maximum rated output to a level that meets the 1MWh energy limit of the device. MS Excel Goal Seek optimization can be used to determine that a new shifted peak of 1,346 kW achieved by reducing the 1,583 kW demand peak by 236 kW (instead of 250 kW) will accommodate the storage device’s 1 MWh energy limit.

Repeating this goal seek optimization for every day of available power/energy data will reveal the new reduced peaks in each month. The monthly power reduction resulting from energy storage is calculated using the following equation:

The monetary savings of demand charge management through energy storage were determined using the following equation:

Works Cited

Baxter, R. (2012, November 28). Author, Energy Storage; a Nontechnical Guide. (M. Banta, Interviewer)

Method for Analyzing the Value of Distributed Energy Storage at the Facility Level – Step 3: Select Energy Storage Devices

Figure 1 shows the current step in the evaluation methodology…

Figure 1 Showing the current step in the methodology for evaluating a facility level energy storage deployment.

Building on the example of the facility housing the software company as the example, the next step is to select energy storage devices that are considered viable. Considering all known factors it is now possible to select energy storage technologies that meet the energy and non-energy requirements of the facility. The economic, environmental and energy impacts and benefits of deploying each selected energy storage technology will be evaluated to get a general understanding as to which technology is most appropriate for the facility. For the purpose of this study the following three technologies will be selected.

• The LightSail RAES V1 – an adiabatic, CAES energy storage device that stores the air in a series of filament wound air tanks that occupy a shipping container form factor that can hold 1 MWh of energy. Though the RAES V1 is not yet available in production, LightSail Energy claims that the system will have a power rating of 250 kW, with a round trip efficiency of 70% and the ability for repeated, deep discharge over a 20 year life span  (Lightsail Energy, 2012).
• Prudent Energy’s Vanadium Redox Battery Energy Storage System (VRB-ESS®) - a vanadium redox flow battery with a footprint slightly longer and taller than a 30 foot shipping container (30.5’ x 6.6’ x 9.3’) (Shipping Containers 24, 2013). The system has a DC round-trip efficiency of up to 85%, with the response time < 50 ms. The power rating starts at 250 kW and can be scaled up by combining power modules. The energy rating is also scalable by increasing the volume of the vanadium electrolyte tanks. 1 MWh of energy requires 61.6 m³ of vanadium electrolyte. The system is rated at 100,000 full discharge cycles (Prudent Energy, 2013).
• Ice Energy’s Ice Bear – Thermal storage device that produces ice at night that is used during the day to augment building AC equipment. The energy benefits realized by deploying an Ice Bear are equivalent to a 7 kW reduction in peak power demand and a total of 35 kWh of energy shifted to off peak. Used daily, the Ice Bear is expected to have a 25 year life span (Ice Energy, 2012).

Works Cited

Lightsail Energy. (2012). Technology. Retrieved January 14, 2013, from Lightsail Energy: http://lightsailenergy.com/tech.html

Prudent Energy. (2013). Prudent Energy’s Vanadium Redox Battery Energy Storage System (VRB-ESS®) Product Brochure. Retrieved January 29, 2013, from www.pdenergy.com: http://www.pdenergy.com/pdfs/Prudent_Energy_Product_Brochure_2011.pdf

Shipping Containers 24. (2013). 30 Foot Shipping Containers. Retrieved January 29, 2013, from Shipping Containers 24: http://www.shippingcontainers24.com/dimensions/30-foot/

Method for Analyzing the Value of Distributed Energy Storage at the Facility Level – Step 2: Non-Energy Considerations

Figure 1 shows the current step in the evaluation methodology…

Figure 1 Showing the current step in the methodology for evaluating a facility level energy storage deployment.

Continuing with the example of the software company, the appropriate application determined in the previous steps is Demand Charge Management. With this in mind only energy storage devices that supply sufficient energy at a power rating enough to significantly reduce the facility’s peak power demand will be considered. This eliminates from consideration low energy/power rated devices and devices that focus on power quality (such as flywheels and capacitors). Of course, all facilities are unique and low energy/power rated devices and power quality devices maybe highly valuable elsewhere. It is also important to note that energy storage device manufacturers are constantly looking at ways to increase the breath of applications their devices can address.

Outside of the energy requirements for the facility there are many other factors that must be considered to choose the appropriate energy storage technology. One important factor is the amount and type of land available for an energy storage implementation. Considering land use, the following technologies were not be considered feasible at the software company:

• Pumped Hydro energy storage - large amounts of land are required for the two reservoirs, one of which must be elevated significantly above the other.
• Gravity Power energy storage - even though there may potentially have been several acres of land available on the facility’s property, this technology is not considered feasible because of the large upfront investment in digging the 500 m deep storage shafts.
• CAES using underground caverns - it is not known if there is a large underground cavern which could support CAES. However, it is unlikely that the facility owners could be convinced to initiate the geologic survey required to determine if there is a viable underground cavern.
• CAES using underwater airbags - though there is a reservoir located near the facility, the reservoir is used to supply drinking water to a nearby city and the mean depth is 3.8 m. For this reason it is not likely that the reservoir can be used for CAES using underwater airbags.
• CST - there is not likely sufficient land and sufficient solar access to support a concentrated solar thermal electricity generation and storage system.

Clearly an appropriate energy storage device would be one with a small footprint that does not rely on unique geologic features. Though it is understood that some changes to the facility may be required to support energy storage technologies, it is not likely that the changes required to add sufficient thermal mass to the building will be considered. For this reason, adding a Trombe wall or some other type of thermal mass is not considered viable.

It is important to reiterate that all the above considerations apply only to the particular facility in question (the software company). Every facility is different and something inappropriate for one facility may be applicable to another.

Method for Analyzing the Value of Distributed Energy Storage at the Facility Level – Step 1b: Electricity Issues

Figure 1 of the previous post (Step1a: Current Energy Needs) shows the current step in the evaluation methodology. After understanding the current electricity consumption at the facility the next step is to understand if there are any adverse conditions, with regards to energy consumption, that could be rectified by deploying energy storage at the facility. Understanding the energy needs at the facility and any electricity issues experienced at the facility, is critical to selecting the energy storage application that would be most profitable at the facility. Understanding the most appropriate energy storage application will, in turn, help to select the most correct energy storage device.

When I first performed this analysis, the facility in question housed a software company. Interestingly, though they were charged a considerable demand charge on their electricity bills they did not pay a different rate for their peak energy consumption vs their off peak consumption. It should be noted that this uniform energy billing was very uncommon in the area. The facility also experienced a statistically anomalous lack of power quality events (especially when compared to previous facilities that housed the same company). Another important element, is that the facility did not deploy onsite renewable energy generation.

Without a difference in Peak vs. Off-Peak billing, utilizing energy storage for the Time of Use (TOU) Energy Cost Management application would be of little value at the facility. Without significant power quality events, power quality applications of energy storage were also of little value. Clearly without on-site renewable energy deployment, the application of energy storage to smooth out variability in renewable energy generation was also not important. At this facility, in its current state, only Demand Charge Management had the potential to be a valuable additional energy storage application.

I say additional energy storage application because as a software company, the facility housed several server rooms which demanded 100% reliability. Even though the grid supplied electricity was considered highly reliable, this demand for 100% reliability required that backup diesel generators be deployed. These generators could produce electricity almost indefinitely (provided a constant supply of diesel fuel) but they had a start-up time of roughly 15 seconds. To ride through the time between a blackout from the utility and the availability of electricity from the backup generators the facility used energy storage in the form of six flywheels in a blackout application.

With an understanding of both the energy needs and energy issues at the facility one can understand the most appropriate applications of energy storage at a facility. Understanding the appropriate application is the first qualifying step for selecting an energy storage device that is appropriate for the facility. Next we will look at other things that must be considered to select an appropriate energy storage device so that the device can be evaluated to understand the value of its deployment at the facility level.

Method for Analyzing the Value of Distributed Energy Storage at the Facility Level – Step 1a: Current Energy Needs

Figure 1 shows the current step in the evaluation methodology…

Figure 1 Showing the current first step in the methodology for evaluating a facility level energy storage deployment.

The first step in evaluating a facility level energy storage deployment is to understand:

• The current power and energy consumption at the facility
• Any issues related to electricity currently experienced at the facility

For a first pass analysis, understanding the current power and energy consumption at the facility can usually be done by reviewing utility bills for the facility. Clearly the larger the interval of time for these utility bills, the better. One year of utility bills will provide some insight to seasonal variation in energy consumption and should be considered the minimum interval. Longer intervals would cover significant, sometimes one time, changes in the facility or trends that span multiple years.

In reviewing the electricity bills it is important to understand the different components of the bill. These components, which are often not seen in a household electric bill, result from  the relatively large power and energy requirements of many facilities. These components also reflect the utility’s desire to incentivize facilities towards the most profitable, ideal and efficient scenario for a grid utility: the (currently unrealistic) ideal where electric demand remains constant with no variation. Utilities employ two components (or billing schemes) for larger, business customers in an attempt to discourage unpredictable consumption with significant time wise variation in power demand. These components include:

1. Demand Charge - measured in units of $/kW, the demand charge is assessed by the highest demand of a customer (kW) in any 15 minute (or sometimes one hour) interval during a monthly billing cycle (NSTAR, 2013) & (Baxter, 2012). This charge is levied even if there is only one such interval in the billing cycle. In many instances, the demand charge ($/kW) will rival the consumption charge ($/kWh) applied during a billing cycle. Clearly the demand charge is meant to provide an incentive for the facility to avoid large, disruptive spikes in their power consumption. A utility must maintain large, costly power generation assets that are often unused, spinning or idle in anticipation of power spikes. It is important to note that the application of a demand charge is made possible through constant (or short interval) metering of the facility’s electricity demand. This constant metering is a unique characteristic of a large consuming facility’s electric bill. It provides a high level of granularity (uncommon in household bills) that facilitates analysis.
2. Peak/Off-Peak Energy  - The second component is to charge more for electricity consumed when demand is highest (i.e. the peak period) than the time period when demand is lowest (i.e. off peak period). The billing rates for peak and off-peak consumption are in units of $/kWh. The daily start and end time of the peak period varies from region to region, but they usually cover normal daytime business operating hours (when demand is highest)  (NSTAR, 2013).

An annual load profile for  a facility might look something like Figure 2. Looking at Figure 2, for this particular load profile, one can see seasonal variation in electricity consumption with a summertime peak for cooling and a smaller wintertime peak resulting from electrical equipment being engaged to support natural gas fired heating. One can also see a weekly consumption pattern where a lack of occupancy and a forced roll back of equipment on weekends significantly reduces demand.

Figure 2 showing the peak minimum power consumption (KW) of a facility.

In this first pass analysis, the current load profile (KW and kWh over time) will serve as the baseline for measuring the changes predicted by deploying energy storage at the facility level. At this very early stage, one application of energy storage stands out has having great potential for a facility level deployment. That application is the behind the meter application of Demand Charge Management.

Works Cited

Baxter, R. (2012, November 28). Author, Energy Storage; a Nontechnical Guide. (M. Banta, Interviewer)

NSTAR. (2013). Billing Rights. Retrieved January 21, 2013, from NSTAR: http://www.nstar.com/residential/customer_information/billing_rights.asp