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Overview of distributed energy storage for demand charge reduction

Published online by Cambridge University Press:  15 February 2018

Said Al-Hallaj*
Affiliation:
CEO, AllCell Technologies, LLC, Chicago, Illinois 60609, USA, and Visiting Research Professor, Department of Chemical Engineering, University of Illinois at Chicago, Chicago, Illinois 60607, USA
Greg Wilk
Affiliation:
Project Engineer, S&C Electric Company, Chicago, Illinois, USA
George Crabtree
Affiliation:
Director of the Joint Center for Energy Storage Research, Argonne National Laboratory, Argonne, Illinois 60439, USA
Martin Eberhard
Affiliation:
Founder of Tesla Motors and Serial Entrepreneur, Woodside, California, USA
*
a)Address all correspondence to Said Al-Hallaj at sahallaj@allcelltech.com

Abstract

The paper presents a comprehensive overview of electrical and thermal energy storage technologies but will focus on mid-size energy storage technologies for demand charge avoidance in commercial and industrial applications.

Utilities bill customers not only on energy use but peak power use since transmission costs are a function of power and not energy. Energy storage (ES) can deliver value to utility customers by leveling building demand and reducing demand charges. With increasing distributed energy generation and greater building demand variability, utilities have raised demand charges and are even including them in residential electricity bills. This article will present a comprehensive overview of electrical and thermal energy storage technologies but will focus on mid-size energy storage technologies for demand charge avoidance in commercial and industrial applications. Of the ES technologies surveyed, lithium ion batteries deliver the highest value for demand charge reduction especially with systems that have larger power to energy ratios. Current lithium ion ES systems have payback periods below 5 years when deployed in markets with high demand charges.

Information

Type
Review Article
Copyright
Copyright © Materials Research Society 2018 
Figure 0

Figure 1. Hourly and average daily demand in the Eastern United States electricity transmission network: the PJM Interconnection (August 2012–July 2013). Generation and transmission equipment is built to handle the annual hourly maximum: 155 GW while the majority of the year this equipment remains underutilized, Source: W. Booth, Energy Inf. Agency (September 2013).1

Figure 1

Table 1. Value of energy arbitrage.

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Table 2. Value of ancillary services.

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Table 3. Utility rate categories.13

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Table 4. 2016 SCE electricity prices for industrial customers.14

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Figure 2. Summer demand charge for residential customers.

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Figure 3. January load profile for a Texan house with high demand reduction potential.

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Figure 4. Large commercial business property July load profile.

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Table 5. Energy storage device parameters.

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Figure 5. The energy required to reduce demand is the area under the power versus time curve. For parabolic demand profiles, reducing demand 20 kW requires less incremental energy than further reduction to 40 kW.

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Figure 6. Battery system configuration for demand charge reduction. PV integration is also possible through a charge controller.

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Figure 7. Lithium ion battery cost over time (Source: Benchmark Mineral Intelligence).

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Table 6. Comparison between different lithium ion battery chemistries.29

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Table 7. Residential energy storage options.

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Figure 8. Secondary battery energy density over time. Lithium ion batteries are currently the most energy dense chemistry with substantial improvement possible by 2020.45

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Figure 9. System configuration for an ice storage system operate in parallel with a direct expansion (DX) air conditioning cycle. During low demand periods, the icemaker works to cool a cycle fluid that freezes water in the ice storage tank. For demand charge avoidance, the pump cycles the fluid to the conventional evaporator to provide space cooling and avoid operation of the DX condensing unit. A phase change material with a higher melting temperature can replace ice and use the DX condensing unit for cooling. This eliminates the extra refrigeration cycle required for ice creation.48 ©ASHRAE www.ashrae.org. ASHRAE Transactions, (Vol 116), (Part 1), (2010).

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Figure 10. Ice Bear 30 Storage unit designed for distributed TES (Source: Ice Energy, reprinted with permission).

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Figure 11. Ice Bear Ice Storage Compartment (Source: Ice Energy, reprinted with permission).

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Figure 12. Ice Bear system configuration. Separate refrigeration cycles for ice generation (3) and normal air conditioning (4) (Source: Ice Energy, reprinted with permission).

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Figure 13. PCC TES System with single refrigeration cycle.

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Table 8. Battery pack type comparison.

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Figure 14. Impact of battery operation on energy costs and demand charge costs for all simulated load cases, Source: Neubauer, Jeremy, and Mike Simpson. Deployment of behind-the-meter energy storage for demand charge reduction. No. NREL/TP-5400-63162. National Renewable Energy Lab. (NREL), Golden, CO (United States), 2015. Reprinted with permission from the National Renewable Energy Laboratory, from https://www.nrel.gov/docs/fy15osti/63162.pdf, accessed August 25, 2017.61

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Figure 15. Payback period for battery systems as a function of energy fraction and power. The battery energy required to eliminate all variability in building load was calculated and different energy fractions from this total were modeled (0.005, 0.01, etc). The plot on the left were high power battery systems simulated to discharge completely in 30 min while the plot on the right fully discharged in 240 min. Each box plot shows the minimum, 25th percentile, median, 75th percentile and maximum payback period across all 98 facilities modeled in the NREL study, Source: Neubauer, Jeremy, and Mike Simpson. Deployment of behind-the-meter energy storage for demand charge reduction. No. NREL/TP-5400-63162. National Renewable Energy Lab. (NREL), Golden, CO (United States), 2015. Reprinted with permission from the National Renewable Energy Laboratory, from https://www.nrel.gov/docs/fy15osti/63162.pdf, accessed August 25, 2017.61

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Table 9. Energy and power ratings of battery systems for BLAST simulation.

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Table 10. Commercial battery system costs compared with NREL simulation.