In this presentation, first I will give you a brief introduction to energy storage applications. Next, I will talk about the common rate structures that are currently used by the utilities in the US including fixed-rate, dynamic pricing and also net-metering programs. Then I will discuss the electricity bill minimization using behind-the-meter energy storage. Finally, I will provide some examples that study the use of energy storage for different types of customers including residential, commercial and industrial customers under different tariff structures.
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Optimal Time-of-use Management for Utility Customers Using Behind-the-meter Energy Storage Systems
1. P R E S E N T E D B Y
Sandia National Laboratories is a multimission
laboratory managed and operated by National
Technology & Engineering Solutions of Sandia,
LLC, a wholly owned subsidiary of Honeywell
International Inc., for the U.S. Department of
Energy’s National Nuclear Security
Administration under contract DE-NA0003525.
Optimal Time-of-use
Management for Utilities
Customers Using Behind-the-
meter Energy Storage Systems
Tu A. Nguyen, Ph.D
Sandia National Laboratories
October 1st, 2018
3. • Power applications
• Frequency regulation
• Voltage support
• Small signal stability
• Frequency droop
• Renewable capacity firming
• Energy applications
• Arbitrage
• Renewable energy time shift
• Customer demand charge reduction
• Transmission and distribution upgrade deferral
Energy Storage Applications3
4. Front-of-meter vs. Behind-the-meter
Image Credit: Navigant
Generation T&D Residential C&I
Front-of-meter Behind-the-meter
• Behind-the-meter refers to the
systems that are located at the
customers’ sites (homes, commercial
and industrial facilities). BTM systems
are usually owned by customers and
intended for customers’ use.
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5. BTM Energy Storage - Policy
Source: Behind-the-Meter Policy and Market Developments, Q1 2018-GTM Research
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6. Utility Retail Rates
• Energy Charge: a charge to customers for the amount of
energy consumed, $/kWh.
• Demand Charge: a charge to customers for their peak power,
$/kW.
• Other Charges: meter and basic customer fees are
independent of consumption, $/month.
Energy Consumption
Peak Demand
Load Profile
Energy
Charge
Demand
Charge
Other
Charges
Residential
Customers
Yes No Yes
Commercial
Customers
Yes Yes/No* Yes
Industrial
Customers
Yes Yes Yes
* Demand charge is often applied to large commercial
customers
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7. Key factors influence electricity price
• Generation cost: fuel cost and
O&M cost of power plants.
• T&D cost: O&M cost of
transmission and distribution
system.
• Weather conditions: impact
both generation and demand.
• Regulations: retail electricity
prices are often regulated by
Public Service/Utility
Commissions.
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8. Utility Rate Structures – Fixed Rate
• Fixed rate: or often called tiered rate is the rate structure
where a constant price is applied to each tier of energy
consumption.
Image Credit: PG&E
Example – PG&E’s Tier Rate
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9. Utility Rate Structures – Dynamic Rate
• Dynamic rate: includes the rate structures where energy and
demand prices are time dependent.
• Utilities’ motivations for
dynamic-price rate:
• Increase customer
satisfaction with options to
reduce energy bill.
• Encourage load growth.
• Reduce peak demand by
load shifting.
• Comply with statutory or
regulatory mandate Image Credit: smartenergy.com
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10. Utility Rate Structures – Dynamic Rate –
Time-of-Use
Southern California Edison – Schedule TOU-D-A
• Time schedules for TOU:
• Hour: peak, part-peak, and
off- peak hours.
• Day: weekdays, weekends,
and holidays.
• Month: summer and
winter
• In TOU pricing, energy and
demand prices are set in
advance for different time
periods.
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11. Utility Rate Structures – Dynamic Rate – Real-
time Price
• In RTP pricing, the price
of the electricity varies
from hour to hour based
on wholesale market
prices (LMP).
ComEd – RTP Patterns
• The price volatility of
real-time prices has
implications for the
economic inefficiency of
other rate structures.
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13. Utility Rate Structures – Net Metering Program
Image Credit - Lowcountry Solar
• Net metering (NEM) programs allow customers who own
renewable energy systems to export their excess energy to
the grid.
• The net energy exported to the grid will be used to offset
the customers’ consumption. At the end of the true-up
period, the customers will be charged/credited for the net
energy usage/surplus.
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14. Utility Rate Structures – NEM 2.0 in California
• NEM 2.0 allows to credit the customers’ surplus energy at
the same price of utility electricity.
• NEM 2.0 removes the 1MW cap and adds one-time
interconnection fee.
• For residential customers, TOU is also required.
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15. • To benefit from dynamic rate structures, the customers
must be able to change their loads in a manner that lowers
their electricity bills without interrupting their operations
(commercial and industrial customers) or sacrificing their
conveniences (residential customers).
How Can Utility Customers Benefit?
Image Credit - Landisgyr
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16. BTM Energy Storage for TOU and NEM
Applications
Renewable Time Shift Time-of-use Management Demand Charge Reduction
NEM customers can increase
their savings by storing the
excess renewable energy
when the load is low and use
that energy later when the
load is high.
TOU customers can
benefit by charging their
batteries during off-peak
and then discharging
them during peak hours.
TOU customers can also
reduce their peak
demand by discharging
during peak load hours.
Image Credit – Aquion Energy
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17. BTM Energy Storage for Reactive Power
Applications
• The latest power inverters are
able to inject/absorb reactive
power while transferring real
power to charge or discharge the
storage device.
• This enables the reactive power
applications for BTM storage.
Examples include power factor
correction, volt/var support.
ABB Energy Storage Inverter – ESI series
(85 kVA to 315 kVA)
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18. BTM Energy Storage - What’s the Catch?
Image Credit – Tesla
91.8%
efficiency
96%
efficiency
88%
roundtrip
• There are energy losses while charging/discharging the
battery.
• The cost is decreasing but still high.
• There are safety concerns from the customers as well as
the first responders.
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19. Minimizing Electricity Bills for Utility Customers
• Given the limitations in storage energy capacity and efficiency,
the economic gains are highly dependent on the storage size
and operation.
• To justify the deployments of BTM energy storage, it is
essential to optimize these factors to maximize the overall
benefits for the customers
Image Credit – Energy Sage
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20. Minimizing Electricity Bills for Utility
Customers
• The objective is to minimize the electricity bill such that the
physical limits of energy storage device and the inverter are
satisfied.
• The decision variables are the charge and discharge power of
the energy storage device at each hour
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21. Minimizing Electricity Bills for Utility
Customers
• Energy storage constraints:
• The SOC must be within its physical limits.
• The final SOC is equal to the initial SOC.
• Energy flow model of energy storage: the SOC
at time i is the sum of the SOC at time i-1 and
the net charging energy.
• Technical Challenges:
• Modeling charge/discharge efficiencies as
functions of operating states (SOC, Temp.,
Input/Output Power)
• Solving optimization problems incorporating
those models.
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22. Minimizing Electricity Bills for Utility
Customers
• Loss model of inverters: inverter loss can be approximated as a
linear function of active and reactive power output
• Inverter’s physical limits:
22
23. Case studies – NEM & TOU Residential Customer
• A typical-size residential customer (3-
bedroom house) in San Francisco is
considered.
• The customer follows PG&E’s E-
TOU/Option-B and NEM 1.0. No demand
charge is applied.
• 5kW PV rooftop system is installed at the
customer’s site.
• $0.03/kWh is applied for the surplus
energy.
Image Credit – Energy Sage
PG&E’s E-TOU/Option-B
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24. Case studies – NEM & TOU Residential Customer
• The energy sold to the grid is reduced by charging the
ESS when renewable energy generation exceed the
customer’s consumption.
• The peak-shaving in this case is not significant because
there is no peak demand charge.
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25. Case studies – TOU Commercial Customer
• A medium-size commercial TOU customer (large hotel) in
San Francisco is considered.
• The customer follows PG&E’s TOU schedule E19.
Demand charge is applied.
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26. Case studies – TOU Commercial Customer
• There are better savings during the
summer months.
• The peak load is significantly shaved
• ESS charges at higher rate during
weekends.
• The total annual bill at each ESS’s
power rating decreases as the energy
rating increases.
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27. Case studies – NEM & TOU Industrial Customer
•An industrial customer in New Mexico is considered:
• A water treatment plant (300kW peak load).
• Solar PV size is 100kW.
•Fixed energy rate and TOU demand rate are applied.
•Penalty is applied for low power factor lower than 0.9
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28. Case studies – NEM & TOU Industrial Customer
• The peak demands during peak hours have been shifted to off-
peak hours.
• The battery inverter successfully maintains the power factor over
0.9 while charging and discharging for TOU management.
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29. Case studies – Is it worth the investment?
• Given the investment cost, what would be the pay back
time?
• Or given the expected payback time, what would be the
maximum investment that the customers should pay?
Case study Storage Size
(kW/kWh)
Interest Rate
(%/yr)
Payback Time
(yr)
Maximum Investment*
($/kWh)
Residential
Customer in SF
5/15 5 10 100
Commercial
Customer in SF
200/1000 5 10 200
Industrial
Customer in NM
200/1000 5 10 150
* O&M cost and tax credit are not counted
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30. Conclusions & Future Work
• With the favorable policies, there are many opportunities for
BTM energy storage.
• BTM energy storage can provide a solution to help the utility
customers benefit from dynamic rate structures:
• BTM energy storage can be used for TOU and NEM
management.
• BTM energy storage can also be used for reactive power
applications such as power factor correction or volt/var support
• Future work in this area involves:
• Impact of large scale deployment of BTM energy storage on
grid resiliency and reliability.
• Optimal control of BTM energy storage in coordination with
electric vehicles, smart home and smart building devices.
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31. References
[1] Tu A. Nguyen, Raymond H. Byrne, “Maximizing the Cost-savings for Time-of-use and Net-
metering Customers Using Behind-the-meter Energy Storage Systems,” in the proceedings of
the 2017 IEEE North American Power Symposium (NAPS), Sept 2017, Morgan Town, WV.
[2] T. A. Nguyen and R. H. Byrne, “Optimal Time-of-Use Management with Power Factor
Correction Using Behind-the-Meter Energy Storage Systems,” in the proceedings of the 2018
IEEE Power and Energy Society General Meeting, Aug 2018, Portland, OR.
[3] D. A. Copp, T. A. Nguyen and R. H. Byrne, “Optimal Sizing of Behind-the-Meter Energy
Storage with Stochastic Load and PV Generation for Islanded Operation,” in the proceedings of
the 2018 IEEE Power and Energy Society General Meeting, Aug 2018, Portland, OR.
[4] R. H. Byrne, T. A. Nguyen, D. A. Copp, B. R. Chalamala and I. Gyuk, "Energy Management
and Optimization Methods for Grid Energy Storage Systems," in IEEE Access, vol. 6, pp. 13231-
13260, 2018.
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32. Acknowledgements
Funding provided by US DOE Energy Storage Program managed
by Dr. Imre Gyuk of the DOE Office of Electricity Delivery and
Energy Reliability.
Colleagues:
•David Copp
•Dan Borneo
•Ray Byrne
•Babu Chalamala
Contact: Tu Nguyen, tunguy@sandia.gov
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