Method for Analyzing the Value of Distributed Energy Storage at the Facility Level – Step 4A: Estimating Energy and Power Savings from Thermal Energy Storage.

Method for Analyzing the Value of Distributed Energy Storage at the Facility Level – Step 4A: Estimating Energy and Power Savings from Thermal Energy Storage.

Now that we have gone through the entire evaluation method for electrical energy storage devices, let’s step back and look at how thermal energy storage devices could be deployed to help reduce energy and power consumption. This will allow for a side by side comparison of these very different technologies. Figure 1 shows the step in the evaluation process to consider thermal energy storage.

Figure 1 Showing the current step (step 4A: Estimating Energy and Power Savings from Thermal Energy Storage) in the methodology for evaluating a facility level energy storage deployment.

For this step in the analysis we will consider Ice Energy’s, Ice Bear (see previous posting on Thermal Energystorage for a brief overview of the Ice Bear). Though Ice Energy’s Ice Bear is a thermal energy storage device, Ice Energy provides a means of estimating the electrical power and energy reductions realized by deploying an Ice Bear. This is done so that facilities managers can consider the Ice Bear alongside electrical energy storage devices. The simple metric is that a single Ice Bear unit can be applied to a 3-5 Ton AC system or a single 5-ton stage of a 7.5-20 Ton system (Ice Energy, 2012). Each Ice Bear unit would provide a 7 kW reduction in peak power demand and a total of 35 kWh of energy shifted to off peak (Ice Energy, 2012).

The first step in evaluating the potential for an ice energy storage system to be retrofitted onto existing air-conditioning units is to understand the size and operating conditions of the existing air-conditioning units. This is first  done in order to size the deployment of thermal energy storage devices. Other information about the existing system can also be helpful. For instance, in the example of the software company, it was helpful to know that there were AC units dedicated to the mission critical data centers in the building. Beyond energy and power saving, deploying Ice Bear units might have the potential to improve the reliability of the AC units themselves, further adding value to a potential thermal energy storage project. It was also discovered that these AC units have the ability to “run free”, or  use cold outside air during the late fall, winter and early spring to provide cooling. Below a certain temperature the compressor and condenser are turned off and outside air is used for cooling. Understanding the temperature at which this switch to “running free” happens is another valuable piece of information. Though the software company is located in New England, the air conditioners are used to cool occupied space almost every day of the year. However, the compressors and condensors are used for cooling only on days when the outside temperature is above the “run free” temperature.

For the purpose of this exercise we will say that using outside air for cooling occurs when the outside specific base temperature is 62°F or less.  During the rest of the year, the energy intensive compressor and condenser are engaged to produce air that is cooler than the outside air. It is during this time (when the outside specific base temperature is above 62°F) that the Ice Bear unit would provide benefit. In estimating the annual benefit of an Ice Bear unit, the duration of the interval requiring the condenser and compressor must be understood. This interval was determined by counting the number of days when there is at least one cooling degree day above the specific base temperature of 62°F. “"Cooling degree days", or "CDD", are a measure of how much (in degrees), and for how long (in days), outside air temperature was higher than a specific base temperature” (BizEE, 2013). For the software company location, from 11/19/2011 to 11/19/2012 there were a total of 1410.3 cooling degree days. During this time, there were 158 days with one or more cooling degree days (Figure 32) (BizEE, 2013). See http://www.degreedays.net/ for one of the many tools out there for calculating cooling degree days.

Figure 2 showing the cooling degree days above the specific base temperature of 62°F (BizEE, 2013).

Using the above estimation, at the location of the software company, each Ice Bear would reduce the peak power by 7 kW and time shift 35 kWh of energy 158 times a year. Though there are some days from November to March that have one or more cooling degree days, it is unlikely that the Ice Bear unit would be engaged on those somewhat anomalous days. With this in mind, the peak power reduction would only occur from April to October (7 months). In those 7 months the peak power is reduced by 7 kW per Ice Bear. The demand charge savings is found using the following equation:

When combined with an AC unit, the Ice Bear surpasses the overall efficiency and performance of the AC unit alone. At night when temperatures are low and thermal efficiency is high, the integrated high-efficiency AC condensing unit is operated to produce ice. During the day, the unit offsets the operation of the energy-intensive commercial AC condensing unit when temperatures are high and efficiency of the AC unit is at its worst. For this reason Ice Energy declares the Ice Bear to be “loss-less”, allowing it to essentially shift 35 kWh of energy consumption from the daytime peak to nighttime off peak hours. For this reason, the Ice Bear essentially has 100% efficiency. Unlike the LightSail RAES V1 and the VRB-ESS®, the demand charge savings do not need to be corrected for additional charge phase energy consumption. The reduction in energy consumption and the associated savings is simply peak energy consumption – 35 kWh per day of operation. From this point LCC should be used to determine the value of deploying a thermal energy storage device. An evaluation of the environmental impacts would follow a methodology similar to what was done for electrical energy storage devices.

Works Cited

BizEE. (2013). Degree Days.net - Custom Degree Day Data. Retrieved February 17, 2012, from Degree Days.net: http://www.degreedays.net/

Ice Energy. (2012). Product Sheet; Ice Bear Energy Storage. Windsor, CO: Ice Energy.

Method for Analyzing the Value of Distributed Energy Storage at the Facility Level – Step 7: Considering the Environmental Impacts

Figure 1 shows the current step in the evaluation methodology…

Figure 1 Showing the current step (step 7: considering the environmental impacts ) in the methodology for evaluating a facility level energy storage deployment.

It is important to consider the environmental impact associated with energy storage technologies. Ultimately one of the primary goals of implementing a renewable energy generation project, balanced by energy storage, is to achieve beneficience, or “the relationship of being a net-positive environmental influence on the planet” (Norris, 2011). Certainly, the overall reduction in emissions related to implementing an energy storage project should be calculated. Because so many factors contribute to the overall reduction in emissions beyond the simple calculation of energy offset by the storage device (including changes in power plant operation; reduction in idle or spinning generator time), such a calculation is rather complicated.

Another important consideration is the Life Cycle Assessment of the energy storage equipment itself. “Life CycleAssessment (LCA) is a technique to assess the environmental aspects and potential impacts associated with a product, process, or service” (US EPA, 2012). An LCA is performed by first compiling an inventory of relevant energy and material inputs and environmental releases; this is often called the Life Cycle Inventory (LCI). Next the environmental impacts associated with each input and release is determined and aggregated to develop an overall understanding of the environmental impacts of the product. 

In order to facilitate the comparison between two essentially different energy storage devices, a functional unit must also be defined. The functional unit describes what each energy storage device must do to be considered equivalent such that a comparison of the environmental impacts of each device using LCA is valid (Norris, 2011). When comparing the LightSail RAES V1 to the VRB-ESS® the functional unit could be to produce 1 MWh of electricity at a power rating of 250 kW every day for 20 years. This functional unit will need to be adjusted to consider other energy storage devices. Considering a combined system of renewable generation and energy storage would require yet another functional unit.

As such nascent technologies, I have not been able to find a full LCA of an energy storage device and without knowledge of the major components that comprise each device an LCA would be difficult to estimate with any reasonable level of accuracy. However the relative environmental impact can be estimated by looking for drivers of environmental impact that are unique in each energy storage device. The LightSail RAES V1, the VRB-ESS® and the Ice Bear are all large pieces of industrial equipment that are each made of various quantities of metals, plastics and other materials that required quantities of fuel and energy for manufacture. A very detailed LCA would be required to determine if one product resulted in reduced environmental impacts compared to the others. There is no single factor in the construction of the power conversion system or the balance of the plant that stands out in one device relative to the others. For this reason, it is assumed that when considering only these two storage device components, the three products would have the same environmental impacts.

The remaining device component, the storage medium, should then be compared across the three devices to see if one stands out above the others. For the Ice Bear, distilled water is the primary storage medium (Ice Energy, 2012). For the LightSail RAES V1, air is the primary storage medium but it should be noted that distilled water is also used to capture, store and release heat required for the operation of the device (Lightsail Energy, 2012). Though producing distilled water does require significant amounts of energy, these devices are closed loop systems so water consumption over the life of the device will not likely be significant. Above these two devices, vanadium redox flow batteries, such as the VRB-ESS®, have a vanadium electrolyte as the storage medium. In a vanadium redox flow battery, the electrolyte solution is commonly composed of vanadium pentoxide (V₂O₅) in a solution of sulfuric acid (H₂SO₄) (Huskinson, 2013). With proper handling, the electrolyte can be fully recycled with little environmental impact but it is the acquisition of vanadium itself that is considered to be the major driver of environmental impacts for a vanadium flow battery (Huskinson, 2013). In a 1998 LCA study by Carl Johan Rydh, it was determined that to meet the functional unit of 150 kWh/day over 20 years, the vanadium electrolyte would have the following environmental impacts:

Table 1 showing LCA results for a study of vanadium electrolyte (Rydh, 1999).

Environmental Impact	Unit	Amount (150 kWh/day)
Global Warming Impact	kg CO2 e	8929
Water Eutrophication	kg PO4 e	6
Air Acidification	kg SO2 e	59

Scaling these impacts up to a 1000 kWh/day system will give an estimation of the environmental impacts attributed to the vanadium electrolyte (the storage medium). The vanadium electrolyte impacts are assumed to be the impacts associated with the VRB-ESS® that are above the impacts of the other two devices.

Recent research out of Stanford University revealed that compared to the amount of energy they can store, the following battery technologies require significant amounts of energy to produce: 

• lead-acid
• lithium-ion
• sodium-sulfur
• vanadium-redox
• zinc-bromine

According to Charles Barnhart, a study researcher, “This is somewhat intuitive, because battery technologies are made out of metals, sometimes rare metals, which take a lot of energy to acquire and purify (Shwartz, 2013).” The study went on to develop a metric called the ESOI (Energy Stored on Investment) which “is the amount of energy that can be stored by a technology, divided by the amount of energy required to build that technology. The higher the ESOI value, the better the storage technology is energetically (Shwartz, 2013)." It was found that a pumped hydro facility had an ESOI of 210: i.e. a pumped hydro facility will store 210 more energy than is required to produce the facility. In contrast, of the electrochemical storage devices, lithium-ion batteries had the highest ESOI value of 10 while lead-acid batteries had an ESOI value of 2 (Shwartz, 2013).

         It is easy for someone to assume that any technology that increases the proliferation of clean renewable energy (as is the promise of energy storage) would inherently be beneficient. However a product that can only store twice the energy that it takes to produce cannot be considered beneficient from an environmental perspective, especially when significantly more efficient alternatives exist. We must strive for the best solution and be willing to change rapidly when better solutions exist.

It is interesting that the same team at Stanford has continued its research into the most appropriate application of energy storage by considering whether it is energetically more efficient to store or curtail solar energy or wind energy. The research found that because solar panels have a significantly less favorable ratio of output energy to energy required for production than wind turbines, it may be a better choice to curtail rather than store wind energy production (and in some instances solar energy production) depending on the associated storage device (Shwartz, 2013). The research indicates that electrochemical energy storage (with the lower ESOI) is more appropriate, from a financial and energetic perspective, for storing solar energy than wind energy across a wider range of scenarios. Charles Barnhart reasons that "You wouldn't spend a $100 on a safe to store a $10 watch. Likewise, it's not sensible to build energetically expensive batteries for an energetically cheap resource like wind, but it does make sense for photovoltaic systems, which require lots of energy to produce. (Shwartz, 2013)"

Works Cited

Huskinson, B. (2013, January 11). PhD candidate, Applied Physics; Harvard School of Engineering and Applied Sciences. (M. Banta, Interviewer)

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

Norris, G. (2011). Doing More Good than Harm: Footprints, Handprints, and Beneficience. Retrieved September 8, 2011, from http://isites.harvard.edu/fs/docs/icb.topic979929.files/Week%201%20Docs/Basic%20Beneficience%20Primer.pdf

Rydh, C. J. (1999). Environmental assessment of vanadium redox and lead-acid batteries for stationary energy storage. Journal of Power Sources, 21-29.

Shwartz, M. (2013, March 5). Stanford scientists calculate the carbon footprint of grid-scale battery technologies. Retrieved November 28, 2013, from Stanford news: http://news.stanford.edu/news/2013/march/store-electric-grid-030513.html

Shwartz, M. (2013, September 9). Stanford scientists calculate the energy required to store wind and solar power on the grid. Retrieved November 28, 2013, from Stanford News: http://news.stanford.edu/news/2013/september/curtail-energy-storage-090913.html

US EPA. (2012, August 5). Life Cycle Assessment (LCA). Retrieved February 15, 2013, from US EPA: http://www.epa.gov/nrmrl/std/lca/lca.html

Method for Analyzing the Value of Distributed Energy Storage at the Facility Level – Step 6: Life Cycle Costing (LCC)

Figure 1 shows the current step in the evaluation methodology…

Figure 1 Showing the current step (step 6: Life Cycle Costing, LCC) in the methodology for evaluating a facility level energy storage deployment.

Life Cycle Costing (LCC) is a methodology that is well suited for evaluating the financial aspects of an energy-saving project when compared to other energy-saving projects. LCC also allows for comparison between the energy saving project and the base case of maintaining the status quo by investing in other projects or saving/investing the money that would have gone into the energy saving project. LCC is the total cost of owning, operating and eventually disposing of the equipment for a project over a given study period. All costs are adjusted using a discount rate that determines the present value of future cash flows. The predicted future change in price of consumed resources during the study period is calculated in an LCC using a regionally relevant escalation rate. The complete evaluation of an energy storage deployment at the facility housing the software company included the comparison (using LCC) of the financial aspects of implementing solar PV, wind energy or one of the three energy storage technologies relative to maintaining the status quo (the base scenario). LCC was also used to evaluate the cost benefit of combining solar PV or wind with one of the three energy storage devices.

For this analysis Building Life-Cycle Cost software, BLCC 5.3-12, was used. This version of BLCC was released in April 2012 by the Applied Economics Office of the US National Institute of Standards and Technology. The software is free to download and a more recent version of the software is available. It can be downloaded here: http://www1.eere.energy.gov/femp/information/download_blcc.html

The following assumptions and common inputs are applicable in a first pass analysis:

• FEMP Analysis – life-cycle costing rules of the Federal Energy Management Program according to 10 CFR 436A can be used in the LCC calculation (DOE, 2012). For a first pass analysis for a facility in the US this should be fine but the applicable LCC rules may be different for each application. A more refined analysis will likely require a review of the appropriate LCC rules.
• Base Date – The base date reflects the beginning of the study period. It is the date when the project(s) would be fully implemented.  
• Study Period – Though each technology has an expected life of at least 20 years, most facility owners/managers have a preference for projects with a payback period less than 2 years. It is this preference for shorter study periods that puts pricing pressure on energy storage devices. The energy storage device manufacturer that can deliver storage devices at a price that allows for meaningful benefit with a payback period within 2 years or less will have a significant advantage over other manufacturers attempting to deliver distributed facility level energy storage.
• Discount/Escalation Rate – The discount rate is set to 3%. As prescribed by 10 CFR 436A, this discount rate is based on long-term U.S. Treasury bond rates averaged over 12 months prior to the annual update of BLCC5. Note that the software determines the escalation rate for energy projects in the given state following guidelines prescribed by 10 CFR 436A (DOE, 2012). It is important to consider that 3% may not be appropriate for a given facility. The discount rate can be estimated by asking those responsible for a facility or organization’s finances. It can loosely be tied to the percentage growth that is commonly realized by investing a company’s cash reserves. Larger organizations may have very conservative rules dictating the investment of cash reserves, however, 3% may still be low for a discount rate.
• Residual Value Factor –The residual value factor represents the salvage or resale value of the project after the study period. For this first pass analysis a residual value factor of zero will provide a conservative estimate.
• Beyond the annual cost of electricity it can be assumed that the operations and maintenance costs of each scenario (including the base case) are the same. For this reason costs related to maintenance and operations may be excluded from consideration in this first pass analysis. This of course, may not be accurate and should be revisited after this first pass analysis.

After entering the required inputs, the LCC software outputs many financial metrics to help evaluate the different scenarios. From the results, the three metrics, generated in the LCC analysis, that are most important to evaluating the financial aspects of a project are:

• Total Present Value (PV) Life Cycle Costs – Considering the time value of money, the Total PV Life Cycle Costs is the cost of the entire project in today’s dollars. Project costs include energy costs and the initial capital investment in the project. Note that the time value of money is driven by inflation and opportunity costs (the benefit the cash could have achieved had it been spent differently or invested) (Fuller & Petersen, 1996).
• Savings-to-Investment Ratio (SIR) - The SIR compares the economic performance for a project alternative by establishing a ratio between the project’s savings and the increased investment costs. The SIR is expressed in present value terms. Only if the SIR is greater than 1 will the project be considered cost effective relative to the base case within the study period. Note that the SIR is also an effective means of comparing one alternative project with other independent alternative projects (Fuller & Petersen, 1996). Though it could be argued that the VRB-ESS® and the LightSail RAES V1 are mutually exclusive projects, they are sufficiently different to be considered independent along with a solar PV project, a wind energy project and the Ice Bear installation.
• Discounted Payback (DPB) – DPB measures the time required to recover initial investment costs with respect to the base case. In DPB, cash flows occurring each year are “discounted to present value before accumulating them as savings and costs (Fuller & Petersen, 1996).” If the DPB is less than the study period, the project is considered cost effective relative to the base case because less present value money is spent during the study period to achieve similar or better results compared to the base case.

Along with changes to energy and power consumption, the most important LCC metrics of an energy storage device are the installation costs (expressed in terms of $/kW or $/kWh), the maintenance costs and any recurring costs related to the storage media. These costs are rarely advertised on a device manufacturer’s website and often require a confidentiality agreement with the device manufacturer. Of all the factors currently limiting the widespread deployment of energy storage, the cost is perhaps the most significant.

Works Cited

DOE. (2012, December 12). Federal Energy Management Program. Retrieved February 23, 2013, from US Department of Energy: http://www1.eere.energy.gov/femp/information/download_blcc.html

Fuller, S. K., & Petersen, S. R. (1996). LIFE-CYCLE COSTING MANUAL for the Federal Energy Management Program. Washington, DC: US Department of Commerce.

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)