Energy Storage Opportunities and Research Needs

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Energy Storage
Opportunities and
Research Needs
Prepared by:
The Industry Technical Support Leadership Committee
June 2020

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Executive Summary
Energy storage has been in use in our society and daily life for decades. Although energy storage
has not grown to be a significant part of the electric energy system, recent advancement of energy
storage technologies and growing needs for energy storage in both power and transportation
sectors make it possible and imperative to accelerate energy storage development, deployment,
and adoption. Power systems have to balance electricity generation and consumption in real-time,
gasoline and diesel fuel are still the primary sources of energy for transportation, and we generally
do not have good ways to conveniently and cost-effectively store a large amount of electrical
energy and use it in an on-demand manner. While we need to continue decarbonizing electric
power generation through increases in renewable generation, we also need to address
transportation as the main source of carbon emissions. Energy storage is an important solution to
address both electrification of transportation and other industries and the variability in renewable
energy such as wind and solar generation.
Bulk of the existing grid energy storage capacity is provided by pumped hydro energy storage
plants that were built to support large baseload power plants such as nuclear generating stations.
Battery energy systems are beginning to be deployed at a rapid pace. The requirements of energy
storage in the electric grid are still evolving and may differ from those of electrical transportation.
Needs for research and development to enhance energy storage performance and knowledge is
summarized in the following areas:
1) Energy storage engineering and integration: Effective system integration is a
challenging problem for energy storage due to the great diversity of potential applications
ranging from behind-the-meter storage to large grid-connected energy storage plants. Each
of these applications has its own set of constraints and performance requirements. Over the
next decade, the diversity of energy storage installations will expand in the range of
applications, in size and scale, and in system complexity. Effective integration is also
important to achieve desired cost reduction needed to support large scale deployment.
Research gaps in this area include: energy storage installations with higher power
capacities and higher working voltages; streamlining engineering to hybridize and co-
optimize energy storage with the rest of the system; more effective controls, sensors, and
energy management systems; designing modular power converter architecture to minimize
system complexity, improve reliability, and reduce integration costs; and industry
standards for secure communication and interoperability.
2) Energy storage modeling and simulation: Energy storage modeling and simulation need
to consider diverse electrical, chemical, mechanical, and thermal subsystems. These are
unique attributes of energy storage in comparison with other conventional generation,
transmission, distribution, and load elements. Both system-level performance and
component-level such as chemical dynamics are important to be modeled and simulated.
Research gaps in this area include: incorporation of energy storage modeling and
simulation into routine transmission and distribution planning and engineering; facilitating
economic analysis and business cases for siting, installation, and operation of energy

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storage systems; developing hybrid data-driven and physics-based modeling for flexible
and adaptive modeling and analysis; models for predicting behaviors of energy storage
components being chemical, mechanical, thermal and electrical; and industry guidelines,
standards, and best practices of using models for evaluating grid applications of energy
storage systems.
3) Energy storage valuation: Valuing energy storage benefits is important but extremely
challenging because of diversity in applications, geographical locations, and energy storage
technologies. Research gaps in this area include: comprehensive approaches on how to
place value on energy storage; compensation strategies for the wide array of services that
energy storage can provide; and energy storage into market rules and resource adequacy
considerations.
4) Energy storage safety and operation: Safety and performance are key for manufacturing,
installation, commissioning, and operation of energy storage systems. Research gaps in
this area include: incorporation of low maintenance principles and intrinsic passive safety
measures into energy storage product design; safety improvement of supporting elements
such as catholytes, anolytes, and seals in energy storage systems; advancing
communication protocols, autonomous controls, and dispatch methods to optimize energy
storage operation and utilization for improved safety; and codes and standards for
installation, training, recycling to improve the safety of energy storage systems such as
those associated with roof-top solar generation.
5) Energy storage technology and manufacturing: Energy storage technology and
manufacturing need to continue advancing to achieve the low cost, large capacity, and long
duration goals for energy storage. Laboratory successes take years to mature for field
deployment, which needs to be accelerated through dedicated efforts. Research gaps in this
area include: advancing energy storage technology for larger capacity, higher efficiency,
and continued cost reduction; developing methods for integrating various energy storage
technologies for diverse use cases; improvement of chemical and thermal stability in
battery storage; and establishing supply chains and the workforce necessary for
manufacturing energy storage systems at scale.
6) Impact on the power grid from energy storage in electrified transportation:
Electrification of transportation is growing from light-duty vehicles to heavy trucks to
airplanes. Beside the requirements of high energy density, light weight, form factors, and
safety for energy storage application in electrified transportation, this trend also has
significant implications for the power grid and lead to new research needs. Research gaps
in this area include: methods for integrating fast charging in the grid; methods and
practices of upgrading the power distribution infrastructure; and managing the increased
uncertainties in electric demand due to the mobile nature of transportation.

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Table of Contents
1 Abstract...............................................................................................................................6
2 Introduction.........................................................................................................................6
3 Energy storage and grid use cases ......................................................................................8
3.1 Energy storage on the electricity grid...........................................................................8
3.2 Incorporating storage into Transmission & Distribution (T&D) and customers
applications.............................................................................................................................9
3.3 Impact of electrification of the transportation sector................................................13
3.3.1 Infrastructure impacts..................................................................................................................... 14
3.3.2 Grid interactions.............................................................................................................................. 16
4 Safety and reliability .........................................................................................................17
4.1 Energy storage safety in research and design ............................................................17
4.1.1 General observations....................................................................................................................... 17
4.1.2 Learning from past energy storage system safety incidents.............................................................. 17
4.2 Improving energy storage safety................................................................................19
4.2.1 General safety principles ................................................................................................................. 19
4.2.2 Planning for the inevitable future abuse .......................................................................................... 19
5 Simulation and modeling ..................................................................................................20
5.1 Modeling ....................................................................................................................21
5.2 Simulation ..................................................................................................................25
5.2.1 Dynamic simulation study examples ................................................................................................ 25
5.3 Economic study examples ..........................................................................................27
6 Technology Gaps and Future Needs ..................................................................................31
6.1 Introduction ...............................................................................................................31
6.2 Engineering and integration of Energy Storage Systems (ESS) ...................................32
6.2.1 Systems engineering........................................................................................................................ 32
6.2.2 Energy storage integration............................................................................................................... 32
6.2.3 Modularity and power conversion systems...................................................................................... 33
6.2.4 Renewable integration with ESS....................................................................................................... 34
6.2.5 Interoperability and cyber security .................................................................................................. 35
6.2.6 Industry standards........................................................................................................................... 36
6.2.7 Applications of energy storage......................................................................................................... 36
Utility microgrids.......................................................................................................................................... 39
6.3 Simulation and modeling ...........................................................................................39
6.4 Valuing energy storage...............................................................................................41

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6.5 Safety and operations ................................................................................................41
6.5.1 Incorporating real-world deployment considerations into early stage R&D....................................... 42
6.5.2 R&D in safety for battery energy storage ......................................................................................... 43
6.5.3 R&D needs for supercapacitor energy storage safety ....................................................................... 44
6.5.4 Communication, training and outreach............................................................................................ 44
6.5.5 End of Life Treatment and Recycling ................................................................................................ 45
6.6 Storage technology and manufacturing .....................................................................45
6.6.1 Battery storage technology.............................................................................................................. 46
6.6.2 Battery chemistry and materials ...................................................................................................... 46
6.6.3 Energy storage manufacturing......................................................................................................... 46
6.7 Implications of electrified transportation ..................................................................47
7 Conclusions........................................................................................................................47
8 Authors..............................................................................................................................49

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ABBREVIATIONS, ACRONYMS, AND INITIALISMS
BEV Battery Electric Vehicles
BESS Battery Energy Storage Systems
BTM Behind The Meter
DER Distributed Energy Resources
EMS Energy Management Systems
ESS Energy Storage Systems
EV Electric Vehicle
MMS Market Management Systems
PEV Plug-in Electric Vehicles
PHEV Plug-in Hybrid Electric Vehicles
R&D Research & Development
T&D Transmission and Distribution

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1 Abstract
Energy storage is a key asset for the future of sustainable and reliable electric energy delivery,
with widespread applications across the grid infrastructure. This document, prepared by IEEE PES
Industry Technical Support Leadership Committee (ITSLC) at the request of the U.S. Department
of Energy, highlights some of the technology gaps and describe future R&D needs for energy
storage to become ubiquitous in the electricity infrastructure. It encompasses various technologies,
tools, and processes, needed to make energy storage systems competitive and easy to adopt and
deploy.
Keywords: energy storage systems and applications, R&D needs, decarbonization, renewable
generation
2 Introduction
Energy storage has been in use in our society and daily life for decades. Pumped hydro for energy
management in power systems, batteries for uninterruptible power supplies and vehicles, and
household batteries for tools and toys, are just a few familiar examples. As power systems have to
balance electricity generation and consumption in real-time and, with increasing penetration of
variable generation, have to handle very fast changes in the supply or demand, there is a need to
conveniently and cost-effectively decouple some of the supply from the demand for electricity.
Energy storage is an important solution to address this. Recent advancement of energy storage
technologies and growing needs for energy storage in both power and transportation sectors make
it possible and imperative to accelerate energy storage development, deployment, and adoption for
electric grid applications.
According to the U.S. Energy Information Administration (EIA), in 2019: transportation
consumed 38 percent of the final energy, mostly fossil; manufacturing consumed 35 percent –
some electricity, some as feed stocks and some fossil; and commercial and residential consumption
made up the balance1
. In a renewable future all of the transportation energy will have to be stored,
either as alternative fuels or in batteries. In manufacturing, carbon-neutral feedstocks will replace
fossil-based ones, and in the commercial and residential world fossil fuels will be replaced with
renewables, either through electrification or alternative fuels.
Today energy storage accounts for less than two percent of how we use electrical energy in all
forms. We have identified various ways to store energy and batteries being one of them. In the
battery world more than hundreds of chemistries are somewhere between lab research and
commercial applications. To go from 2% to 70-80% will require all available forms of storage
technology. Storage will be an integral part of the energy system. Without it, society will never
meet its renewable goals at a reasonable cost.
1
https://www.eia.gov/energyexplained/us-energy-facts/

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Power systems need adequate buffers for handling the variability in renewable energy such as wind
and solar generation. The rapid growth of wind and solar generation changed the predictable nature
of energy sources and feeding renewable energy onto the grid is vastly different than scheduling
conventional hydro and thermal generation. The real-time balance of generation and consumption
in power systems is becoming more and more challenging due to the increased percentage of
variable generation. It is also challenging to handle the long-term variability of wind and solar
generation due to their weather dependency and their diurnal and seasonal fluctuations. Energy
storage for both short-term and long-term power and energy applications is extremely important
and considered the ultimate solution for these power system issues. In the transportation sector,
deeper decarbonization drives a significant development of electric vehicles. This requires new
energy storage technologies that are capable of high energy and power density, high capacity, fast
charging, smaller form factors, and lower costs. As the importance and requirements of energy
storage technologies in the electricity infrastructure are being recognized and may be different than
those of electrical transportation, it is timely to accelerate development of an array of energy
storage technologies.
The needs of energy storage applications can be met by a variety of technologies: electrical,
chemical, thermal, and mechanical; in sizes from kilowatts to gigawatts. Different types of energy
storage technologies are suitable for different purposes, and collectively they support a wide range
of power and energy applications. Some key use cases in the electric grid include firming up
renewables, energy arbitrage, grid investment deferral, grid reliability and resilience support.
Driving ranges, vehicle charging and charging infrastructure are important aspects for applications
in the transportation sector. Energy storage systems can often be used for multiple functions or
purposes thus providing options for more cost-effective applications.
Safety performance is one of the key factors for broader deployment of energy storage. Because
of the high capacity and high energy density, many energy storage technologies present safety
hazards such as electric shocks, fire, corrosive and toxic materials, which in turn present challenges
for personnel and manufacturing. Inadequate modeling and simulation are barriers to
understanding where and how to best deploy energy storage.
Today we are in the experimental stage of energy storage regulation and remuneration, with
different states and independent system operators trying different policies and pricing strategies.
More standardization is required to assure successful, broad implementation
Electricity by its nature has to find a use or be stored in less than a second. If electricity from
renewables cannot be immediately used or stored, it must be curtailed, and such losses will start
consuming. Storage is an important part of the solution and will help stabilize market prices,
availability and, depending on where it is deployed, can provide additional reliability and
resiliency.
This paper reviews use of storage in electric grid applications, including issues, opportunities, and
Research & Development (R&D) recommendations.

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3 Energy storage and grid use cases
The grid has been evolving towards a new mix of generating resources, delivery networks, and
consumption devices, driven by economic development and environmental sustainability as well
as deep electrification. Wind and solar generation continue the rapid growth that started in recent
years2
. New electric uses such as electric vehicles are increasing their shares of electric loads3
, and
more conventional uses are turning into “flexible loads” -- active participants in increasing
economic efficiency and supporting grid operation4
.
3.1 Energy storage on the electricity grid
Significant flexibility is required to reliably run the grid of the future. Energy Storage Systems
(ESS) offer promising solutions by providing a buffer for short-term and long-term energy balance
in the grid. ESS can be used in all stages of the power system and have seen significant cost
reduction, better power and energy performance, and more commercial availability5
. California
achieved its 2020 1,325MW energy storage goal ahead of time6
, and projects 55,000 MW of new
storage by 20457
. However, it is important to note that, while ESS installations in California were
mandated by the regulators, future deployment will require accurate cost-benefit analysis to
identify optimal applications, locations, and size of storage for efficient and effective use of ESS.
ESS will continue to advance and increase in the grid, which is essential for reliable and efficient
grid operation.
ESS can come in many different forms – electric, thermal, chemical, potential, and mechanical8
.
Each has its unique characteristics and offers different capabilities to support the grid. Collectively,
ESS has a broad use in the grid, including providing reliability (also called ancillary) and resilience
services, firming up variable sources, mitigating diurnal concern with renewable energy, enabling
energy arbitrage, providing consumer flexibility, providing power quality services, and deferring
infrastructure additions and increasing infrastructure utilization.
2
REN21 Renewables Now, “Renewables 2019 – Global Status Report”, May 2019. Available at:
https://www.ren21.net/wp-content/uploads/2019/05/gsr_2019_full_report_en.pdf.
3
U.S. Energy Information Administration, “Annual Energy Outlook 2020 with projections to 2050”, January 2020.
Available at: https://www.eia.gov/outlooks/aeo/pdf/aeo2020.pdf.
4
Smart load development. http://www.ieadsm.org/wp/files/IEA-DSM-Task-17-Subtask-10-role-and-potentials-
2016-09-29.pdf
5
H Rudnick and L Barroso, “Flexibility Needed: Challenges for Future Energy Storage Systems”, IEEE Power and
Energy Magazine, September/October 2017.
6
See for example California Public Utilities Commission; https://www.cpuc.ca.gov/General.aspx?id=3462
7
Phil Pettingill, “Ensuring RA in Future High VG Scenarios – A View from CA”, ESIG Spring Workshop. April 10, 2020.
8
Energy Storage Primer, IEEE PES, April 2020; https://resourcecenter.ieee-pes.org/technical-publications/white-
paper/PES_TP_WP_NRG-Storage.html

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As ESS becomes more cost-effective and viable, it is increasingly important to use and further
develop procedures and algorithm to optimize siting and sizing of ESS, as well as perform accurate
benefit-cost analyses, including lifecycle economics and market participation benefits9
. New and
advanced modeling and simulation methodologies and tools, including time-series tools (e.g.
hourly) analyses, are required for accurate evaluation. While some algorithm has been developed
and are available from several commercial organizations, simple integration with T&D planning
remains challenging and the algorithm are not easily used by utility planning staff without support.
Certain technical and economic requirements may be an obstacle to faster adoption of ESS
technologies and their most effective applications. For example, “shared applications” –
utilization of the same ESS asset for several applications – are important in realizing the best
economic potential from the technology. As ESS can benefit generation, transmission,
distribution, or end-user applications, changing rules to accommodate “shared” applications could
be beneficial for consumers.
3.2 Incorporating storage into Transmission & Distribution (T&D) and customers
applications
Energy storage can support a variety of applications, including energy, power, or ancillary
services. Energy-oriented applications focus on longer duration operation such as energy price
arbitrage, wind and solar integration, grid investment deferral, congestion relief, and asset
optimization. Power-oriented applications rely on short bursts of output to balance the grid and
quality of power. Ancillary services include frequency regulation, spinning and non-spinning
reserves, black-start, and voltage regulation. Energy storage has unlocked new value to enhance
the electric grid and lower the cost to serve customers.
Pumped storage has historically been used to flatten the generation profile and allow nuclear plants
to run continuously even through low-load night and weekend periods. The flexibility of pumped
hydro allowed it to perform balancing and regulation functions as well. A primary attribute of
storage for any T&D application is location and the siting issues and geographical limits associated
with pumped hydro can make it less able to address T&D applications. However, this outlook may
be changed by new siting concepts for pumped hydro. The largest storage project in California
was done by the San Diego County Water Authority using drinking water reservoirs and existing
pipelines to provide 500 MW and 20 GWh of storage.10
With the continuing reductions in the cost and increased availability of battery storage there is
growing interest in many states in the use of storage systems to defer Investments in the T&D
system, improve performance and utilization of the T&D system, and improve customer reliability.
More than just batteries have to be considered. Flywheels for power quality, thermal storage for
9
J. Romero Aguero, D. Novosel, E. Bernabeu, B. Chiu, J. Liu, V. Rabl, T. Pierpoint, D. Houseman, B. Enayati, S. Kolluri:
“Managing the New Grid,” IEEE Power and Energy Magazine, July/August 2019.
10
https://www.sdcwa.org/emergency-storage-project

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load shifting, high pressure air for demand management at manufacturing facilities, and even use
of community drinking water reservoirs for pumped storage need to be considered.
These applications have to be justified in an engineering and economic sense as compared with
traditional T&D solutions and are often called “Non-Wire Alternatives”. In some cases, the storage
can perform “stacked applications” as a secondary purpose to improve the economics – that is,
engaging in energy arbitrage or ancillary services. Key T&D applications are described below
along with key characteristics of the battery storage technology required. It should be noted that
when providing almost any T&D application the consequence of failure can be a reliability event
where other T&D assets are loaded to emergency ratings and/or a loss of load in the worst case.
Therefore, the storage technology as deployed has to be completely reliable and/or backed up by
redundant resources.
Grid investment deferral – One class of applications is to defer investments in grid assets to meet
peak capacity as load grows, where the time value of the deferred investment in capacity is
compared to the cost of deploying energy storage for that period at proper locations. Here the
longer the deferral period, the more energy storage is needed in a generally quadratic relationship
(the area under the curve above the rating) so the optimal economics is achieved when storage
costs are balanced with the net present value of the deferred investment, usually measured as the
annual revenue requirement. At the end of the deferral period, the storage asset must be dedicated
to other (market) functions; removed and written off; or removed and relocated to a new site. For
instance, there is an upcoming 70MW energy storage system to be built in California to optimize
renewable energy system performance and maximize the return of investment.11
Another example
in Australia where the energy storage surges had helped the French renewables developer to
generate around 61% year-on-year increase in revenues of the 2020 first quarter revenue.12
The
use of a four-quadrant “smart inverter”, which could control both active and reactive power, in
conjunction with storage, can gain additional capacity by improving power factor along the circuit.
This also reduces losses and is less costly than traditional upgrades. An example of the sizing
relationship for capacity deferral is shown at Figure 1.
Congestion relief: Storage can also be used to provide congestion relief for transmission. Here,
the consequence of a storage system failure is increased congestion costs in general, not a
reliability event per se. Unlike peak shaving on a radial distribution system where the storage must
provide 100% relief it is possible to relieve only a portion of the congestion. Thus, the battery
sizing and costs are balanced against the amount and the frequency (hours per year) of the
congestion relieved.
11
https://www.wartsila.com/media/news/29-04-2020-wartsila-is-delivering-a-70mw-energy-storage-system-in-
california-to-optimise-renewable-energy-system-performance-and-maximise-roi-2696780
12
https://pv-magazine-usa.com/2020/05/20/hornsdale-and-its-big-tesla-battery-exceed-expectations-as-neoens-
storage-revenue-surgesneoen-reports-strong-revenue-increase-teslas-hornsdale-big-battery-exceeding-expectations/

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Figure 1. Example of grid investment deferral analysis
Voltage regulation: Improving the voltage profiles on the distribution system via energy storage
instead of using capacitors or tap-changing transformers. Storage is generally not competitive with
traditional solutions for minor voltage issues but if reconductoring / repowering is necessary then
the economics are similar to capacity deferral.
Reliability and resilience enhancement: Storage can be used to improve the reliability and
resiliency of the T&D system. This is important for situations where higher inertia rotating
generators have been supplanted. It is also important for the grid to survive large disturbances such
as natural disasters and cyberattacks. There are already numerous instances where storage has been
deployed for maintaining frequency and voltage, suppressing transients and oscillations, and
sustaining against contingencies during or after an outage, both at the transmission and the
distribution levels. This application has been pioneered by utilities for many years. The American
Electric Power (AEP) Presidio installation was an early example.13
The typical application is
where one or two lines serve a remote community and either a single line is no longer able to carry
the peak load in event of an outage, or the possibility of the loss of both is too great due to shared
right of way. In these cases, large storage systems (tens of MW good for 6-8 hours) are used to
carry the load until faults can be cleared and damage repaired. In the distribution space, storage
can be used when a single circuit serves a load and there is no easily installed alternate circuit to
carry N-1 contingency roll over. Such opportunities arise at the edges of service territories or on
peninsulas, for instance.
13
2017 AEP Corporate Accountability Report / Energy Storage, https://www.aepsustainability.com/

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Renewable energy resources integration: Energy storage can act as an enabler to firm up
variable sources like wind or solar generation. It can provide support on demand to reduce the
effects of fluctuations in wind or solar generation, so that renewable resources become comparable
to more conventional generation options that have a fixed capacity. The location of the storage for
firming renewable sources is not fixed. The storage may be collocated with the renewable
generation, where the facility operator is paid more for power that can be dispatched when needed;
or it may be closer to the consumer to avoid congested transmission lines. For instance, the
Colombian energy authorities have targeted the Caribbean port city of Barranquilla, capital of
Atlántico department, to install BESS reinforcing electricity supply in the Caribbean region due to
the principal growing load centers but is challenged by dispatch issues.14
Storage may be in the
form of a single large system, or an aggregation of many smaller systems, such as residential
batteries. Beside short-term variability which needs firming services, renewable energy has long-
term variability that also has to be managed. Both wind and solar generation can be seasonal with
significant difference throughout the year and it is also depending on the continents’ weather.
These diurnal and seasonal issues exist today, but their share is small and conventional generation
can be used to mitigate these issues. When wind and solar become dominant resources as the
current trends indicate, these diurnal and seasonal issues have to be mitigated by other means, such
as ESS.
Hosting capacity improvement: Improving the PV hosting capacity of the system by using
storage to mitigate reliability issues introduced at high PV penetration – high voltage, flicker, and
sometimes back-feed. Here the four-quadrant inverter again provides increased benefits and
lowers costs.
Mitigating transient stability phenomena: Transmission capacity can be limited to below its
maximum due to short term dynamic disturbances occurring after a fault such as a lightning strike.
There are pilot projects on this application. It is well suited for storage as the system (Could be
provided by super caps, flywheels, or SMES) can be fully charged waiting for the event – the
charge/discharge cycling and degradation will not be an issue and the total energy capacity is low
as the duration of the transient event is very short.
Transmission VAR Support: Energy storage could be used to enhance the T&D system
performance by providing support during the event of electrical anomalies and disturbances such
as voltage sags, unstable voltage, and sub-synchronous resonance.
Black-start application: Black start during power system restoration after a major disturbance
such as blackout requires idle power plants to be energized without support from the grid.
Traditionally, small diesel generators are used to start larger generators to provide a black start.
14
https://www.bnamericas.com/en/news/colombia-advancing-energy-storage-plans

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Integrating energy storage with the power plant for black start can support the restoration of the
grid effectively and improve the overall reliability and resiliency of the system by responding very
quickly. Another aspect of black start, particularly in weak grids, is energizing transformers and
lines, where storage can cover the inrush more effectively. Other options for use of storage
(including customer-side storage) in supporting the grid include.15
Arbitrage: The objective is to reduce costs by storing lower price electricity for use in higher price
periods.
Demand response: Using storage provides options to almost instantaneously reduce demand by
relying on customer-side storage or control resources.
Microgrids: The utility service provider or a customer uses a combination of generation and
storage to serve load. A microgrid should be able to disconnect from the electric grid but continue
to carry the load. Examples of microgrid installations are: campus-level, distribution feeder
(particularly at isolated feeders) or substation level, transmission network feed (60 kV and higher)
or "neighborhood" level, distribution network feed (below 60kV), or local facility level at
secondary utilization voltages.
Responding to time-of-use or demand charges: Utility customers can use energy storage to
modify their electricity use profile so as to take advantage of electric rates to reduce their electricity
costs.
Service reliability: The need for backup power at customer facilities. Usually, larger facilities use
a combination of batteries for ride-through of momentary outages and a backup generator for
longer duration outages.
Power quality: Power quality problem may cause a mis-operation or failure of sensitive industrial
equipment and critical commercial operations. Energy storage can be used to improve power
quality against short-duration events such as harmonics, flicker, or variation in voltage and
frequency.
All these applications require detailed engineering analysis in order to size the storage power and
energy capacity correctly. Today only a modest fraction of the many possible uses is practical
when compared with conventional solutions due to the costs of storage.
3.3 Impact of electrification of the transportation sector
The sales of Plug-in Electric Vehicles (PEVs) are increasing, with established auto manufacturers
and newer Electric Vehicle (EV) companies are offering a wide range of options. PEVs are
vehicles that depend mainly on outside sources of electricity (plugs) for electric propulsion. They
do not include conventional hybrids or other vehicles where most of the electricity needs are
15
Energy Storage Primer, IEEE PES, April 2020; https://resourcecenter.ieee-pes.org/technical-publications/white-
paper/PES_TP_WP_NRG-Storage.htm

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generated on-board the vehicle. PEVs include Plug-in Hybrid Electric Vehicles (PHEVs), which
use both electricity and fuel, and Battery Electric Vehicles (BEVs), which rely completely on
outside sources of electricity stored in batteries. There is growing acceptance of EVs by the general
public, with low cost of ownership helping to overcome range anxiety. In its current EV program
launch, automotive manufacturers claim to have driven battery cell costs below the $100/kWh
level, signaling further decline in PEV purchase price.16
The growing EV market will have a
profound impact in ESS usage and influence on the grid.
3.3.1 Infrastructure impacts
A wholesale change in transportation energy from fossil fuels to electricity presents some major
infrastructure challenges. EV owners expect the transition to be as painless as possible, and while
overnight charging at home is convenient, charging during longer journeys can present obstacles.
Conventional gasoline fueling typically adds around 400 miles of range in five minutes, and the
aim is to get as close as practical to that result with EVs.
Such a result is possible with many alternate fuel vehicles, where the existing network of filling
stations could be repurposed. This repurposing is more difficult for fast charging of BEVs because
the electrical service to some gas stations would not support this. Tesla has been active in installing
about 20,000 chargers, at various levels.
Load forecasts are showing that electrification can potentially lead to national annual growth rates
in electricity usage of .65% in the conservative case to 1.2% in the medium case and 1.6% in high
adoption cases.17
Though by historical standards, this growth rate is lower it still will have a
significant impact on utility planning and it may be necessary to add capacity to the local
distribution infrastructure. An alternative is to install Distributed Energy Resources (DER),
including distributed generation and storage, electrically downstream from congestion points.
The possibility of infrastructure expansion may be limited in urban areas, due to space constraints
and congestion in the local transmission and distribution system. In the future there may be
dedicated highway lanes with embedded charging capability, where EVs can be charged while
traveling at high speed. This capability would be a major solution for range anxiety.
Of more immediate concern is some of the existing distribution infrastructure, which may not be
able to accept PEVs without some rework. The peak demand imposed by the PHEV and BEV on
the grid depends on the size of the on-board battery, the owners’ driving patterns, the charging
strategy, and the charger characteristics. A number of studies have developed, and continue
16
https://www.greencarreports.com/news/1126308_electric-car-battery-prices-dropped-13-in-2019-will-reach-100-
kwh-in-2023
17
“Electrification Futures Study (EFS): Scenarios of Electric Technology Adoption and Power Consumption for the
United States,” NREL, May 2017, https://www.nrel.gov/docs/fy18osti/71500.pdf.

15
developing, the actual electricity use data needed to establish the impact on the power system.
With PEV “efficiency” of 3-4 miles/kWh EVs use approximately 2,700–3,300 kWh per year, or
much less than a standard electric water heater. The more powerful chargers will result in much
higher demand than that imposed by charging through a conventional plug. Demand management
measures to enforce load diversity could prevent a possible overload. Ample experience already
exists with the success of such controls, which have been widely applied to off-peak heating and
water heating.18
To understand the full impact of electrification on infrastructure, the charging infrastructure needs
to be assessed. Studies are usually focused on residential charging because 80-85% of all charging
for cars is expected to occur at homes with at most, Level 219
charging (3-7kW). Fast charging is
then incorporated along highway corridors and in some public areas. In addition, light-duty,
personal use vehicles are expected to be the majority of electric vehicles deployed.
Light duty vehicles are expected to be charged at residences, where chargers will max out at 10kW
for Level 2 charging, so it may not be difficult to estimate loads and “spread” it across a utility
territory. However, the challenges are greater for commercial and fleet vehicles due to vehicle
battery sizes and logistics. Fast charging becomes a requirement for those vehicles. Though it is
understood that on aggregate level, fleet applications make up a smaller segment than personal use
vehicles in total number of vehicles, the charging sizes to support the segment will be larger. DC
fast chargers were first deployed at 50kW, but are now being introduced at sizes of 150kW,
250kW, with announcements of 350kW chargers. Though personal use vehicles are not even able
to accept charging rates greater than 150kW, companies such as ABB, eFacec, the Inonity Alliance
(BMW, Ford, Daimler, Volkswagen), Tesla, and Porsche all have plans or have announced plans
to offer chargers above 300kW – 30 times the size of standard Level 2 home chargers. Edison
Electric Institute (EEI) predicts 9.6 million charging stations divided among Home Level 2 (78%),
Workplace Level 2, Public DC Fast, Public DC Level 220
. The Department Of Energy (DOE)
National Renewable Energy Laboratory (NREL) National Plug-In Electric Vehicle Analysis
utilizes the NREL EV (EVI-Pro) charger model to regionalize the predictions, also targeting 2030
and 18 million vehicles projected to be on the road21
. The analysis projects higher home charging
fraction (82-88%) but also determines DC fast charging via spatial analysis by predicting the
number of charger types necessary and distributing the chargers across an area.
The increasing size of chargers changes the dynamic of evaluating the impact of charging stations
on a grid. Chargers will impact the grid across five (5) segments:
18 See, for example, IEEE Tutorial Course: Fundamentals of Load Management/89Eh0289-9-Pwr (1988)
19 The various PEV charging alternatives are discussed in Developing Infrastructure to Charge Plug-In Electric
Vehicles, U.S. DOE Alternative Fuels Data Center; https://afdc.energy.gov/fuels/electricity_infrastructure.html
20
“Electric Vehicle Sales Forecast and the Charging Infrastructure Required through 2030,” Edison Electric Institute,
November 2018
21
“National Plug-in Electric Vehicle Infrastructure Analysis,” DOE EERE, NREL, September 2017

16
1) Light Duty Vehicles: mostly Level 2 charging – approximately 85% charging at residences
and a one to one relationship – where planning impact is estimating by the year on year
vehicle adoption multiplied by the size of the charger (Level 2 – 7.5kW)
2) Workplace Charging: where business and office parking structure will provide Level 2
chargers for daily charging of parked vehicles and 2-4 fast chargers for workers looking
for a quick charge before leaving work
3) Public Charging Plugs (slow): typically, free charging sites
4) Corridor Charging: along highways, aimed at fast, short duration charging. The typical
size charger will be a minimum of 150kW (30-minute charging target), located along
highway travel plazas, and will contain 2-4 chargers
5) Fleet Charging: Charging that will occur at fleet depots, from medium size to light duty
trucks. Minimum 10 chargers per site, likely 50kW + chargers. Sites will occur at
distribution centers.
3.3.2 Grid interactions
EV charging will provide a foundation for incorporating flexible (managed/smart) charging into
grid operations. The storage may also help support higher penetration of variable renewable
resources. Widespread installation of charging stations at workplaces would provide additional
load during peak hours of solar production, thus avoiding curtailment and helping to address the
problem of the so-called ‘duck curve’22
.
Smart EV charging will be essential to avoid adding load during periods of peak electrical demand,
and it would also allow the provision of grid services. While performing their EV charging
function, smart chargers, particularly with many units aggregated under common control, could
modulate their output in response to a signal from the grid operator, increasing or decreasing the
overall charging power to help balance minute-by-minute variations in generation or demand. Not
only would this service help stabilize the grid, but it would also provide an additional income
stream to the operator of the charging points.
Smart charging does not require two-way power flows, where the EV battery would both charge
and discharge. Discharging the battery, other than for its primary purpose of powering the vehicle,
would potentially void the manufacturer’s warranty. One possibility for enabling Vehicle-to-Grid
(V2G) operation would require manufacturers to switch to a throughput-based warranty, covering
a certain number of kilowatt-hours of discharge over the warranty period. Such a change would
allow vehicle owners with shorter commutes to offer their batteries for two-way grid services,
thereby realizing some income to offset their operating costs. Beyond grid services, two-way
power flows would enable emergency Vehicle-to-Home (V2H) operation, where the vehicle
battery could interact with rooftop solar to provide microgrid functionality during grid outages.
22
See https://www.energy.gov/eere/articles/confronting-duck-curve-how-address-over-generation-solar-energy

17
While much of above discussion pertains to light-duty vehicles, electrified trucks are beginning to
enter the market. Different considerations also apply to fleets, whether autos or busses.
4 Safety and reliability
Almost all energy storage technologies have potential safety issues. For example, flywheels can
pose an explosive fragmentation hazard, and thus must be properly contained or crane-hoisted
weights can fall with crushing force and fragmentation hazards. Batteries typically have chemical
and fire-safety issues. If R&D (and engineering) does not adequately minimize risk, fire and
building codes will do it for them, often with draconian results that greatly limit the deployment
of the technology by either making it too expensive or space-consuming to deploy with all the
secondary and tertiary safety measures. It is much better to design for the highest level of inherent
safety in the energy storage device itself so that the fire and building codes do not have to take
drastic add-on safety measures.
4.1 Energy storage safety in research and design
4.1.1 General observations
For battery energy storage, managing the universal tradeoff between energy and power without
sacrificing safety and cycle life remains a daunting task. Batteries, particularly lithium-ion
batteries, present specific safety challenges. First among these is that a battery system cannot
simply be shut off. Truly deenergizing a system typically is not possible. Managing the residual
stored energy provides a specific challenge. Safety through engineering becomes of paramount
importance. In addition, there needs to be a sustained effort to develop standards and codes for
engineering and deployment of energy storage systems.
Designing for safety begins at the fundamental building block level with the component selection,
moves up through integration of the components to the module, rack, and system levels, and
considers issues that will arise and/or should be addressed in installation/commissioning, plus
operations and maintenance including incident preparedness for both the user and the first
responders. It is worth noting that the cost of corrective actions increases dramatically as the final
safety mitigant moves from components up to the system level. Thus, R&D in safety design should
be focused at the most fundamental component levels first such as ceramic-coated separators for
Li-ion batteries.
4.1.2 Learning from past energy storage system safety incidents
Previous experience with the introduction of energy storage technologies has shown that “we don’t
know what we don’t know yet”. In other words, many of the unsafe things that have occurred with
deployed energy storage technologies were not even worried about as failure modes that might
need to be tested for until they occurred after many real-world deployments. Using Li-ion batteries
as an example, they have been commercially available since 1992 in portable devices, and cell-
level testing for safety (e.g., UL 1642) was developed early on. This focused on making sure that
cells vented the gasses developed during a thermal runaway event due to short circuit, overcharge,
etc. instead of exploding. However, it wasn’t until almost 10-15 years after the cells began to be

18
deployed in large scale modules/systems that the effect of those hot vented gasses on nearby cells
leading to cell-to-cell propagation, and a conflagration due to the large number of cells where each
of which store lots of energy and fuel was fully realized, and testing regimes to ensure that cell-
to-cell propagation of fire was limited were developed. Only since 2018 has there been a
recognized cell-to-cell propagation test, and designing modules/systems to prevent this, and then
testing for it, is crucial from a fire safety perspective.23
Along the same lines, most deployments of Li-ion batteries in large-scale energy storage have used
ISO specifications for containers with clean-agent fire suppression. The clean agents such as fully-
fluorinated ethyl isopropyl ketone, or heptaflouropropane that replaced the banned fluorocarbons,
including Chlorofluorocarbons (CFC) and Halon, as the primary method for non-sprinklered
permanently-installed fire suppression have some cooling properties and oxygen deprivation
effects, thus suppressing fire growth. However, they are expensive, and thus not much more is
provided than is necessary to suppress the fire. Insights from several real-world energy storage
container fires showed that the clean agents did their jobs at suppressing the fire initially, but
ongoing propagation could result in continued venting of flammable gases, and when oxygen was
introduced typically by opening the door, or another breach, an explosion could result, sometimes
with catastrophic consequences. So, the new thinking is to pair clean agents with water
suppression (the latter either by plumbed sprinklers, or by fire department hookup to a standpipe
for sites remote from a water source).Hence, it could be argued that cell-to-cell propagation in Li-
ion batteries, for example, should have been foreseen and mitigated against early in both the design
and testing regimes.
Another lesson from past energy storage / stationary battery deployments that needs to be applied
to future Battery Energy Storage Systems (BESS) R&D is the production of flammable/explosive
and toxic gasses by aqueous battery technologies, especially under abuse and/or thermal runaway
conditions. For example, lead-acid including Valve-Regulated Lead-Acid battery (VRLA),
sometimes incorrectly called “sealed” and Ni-Cd batteries, even under normal conditions, produce
small amounts of hydrogen gas. This gas production means that the container for the batteries
cannot be completely sealed, and while active ventilation may not be required, passive ventilation
will be required at a minimum. Lead-acid batteries are very susceptible to those conditions,
especially towards the end of their life or during prolonged overcharging typically due to improper
float/equalize voltage settings or equalize time periods. Lead-acid and Ni-Cd batteries can produce
enough hydrogen to drive the need for active ventilation to ensure hydrogen production does not
cause a buildup in the space that exceeds the Lower Flammability Limit (LFL). In addition, under
thermal runaway conditions, lead-acid batteries can produce toxic hydrogen sulfide gas, and
antimony-containing VLA batteries can produce toxic arsine and stibine.
23
Modelling and experiments to identify high-risk failure scenarios for testing the safety of lithium-ion cells,
https://www.nrel.gov/docs/fy19osti/71712.pdf

19
Flow batteries can produce flammable/explosive quantities of hydrogen as well as toxic gasses
(the latter dependent on the flow battery chemistry). Existing research has not well quantified the
amount of hydrogen production of various flow battery systems, and thus near and medium-term
research should focus on refining these numbers to allow for economical design of ventilation
systems to control buildup. Designs should minimize the potential for buildup of these gasses, and
not assume that a failure mode or user abuse that could drive dangerous levels of
flammable/explosive or toxic gasses will not occur. Guidance must be given to the user as to how
they can detect and control the buildup of these potentially dangerous gasses.
4.2 Improving energy storage safety
4.2.1 General safety principles
Because energy storage can be on both the consumer/customer side as well as the utility/industrial
side, it should be noted that electrical safety considerations for the two sides are different.
Residential consumers including in automotive energy storage should never be exposed to voltages
above nominal 375 VDC or 250 VAC rms. If voltages higher than that are used, the design of
connectors (hopefully “standardized” connectors) should make it nearly impossible for consumer
users to contact those voltages.
When enough battery cells are connected together in a series string, voltages can be a personnel
concern. Most batteries cannot be made safe via lock-out/tag-out, as they still retain high amounts
of energy even after being “disconnected”. Arc flash/blast and shock safety can be improved if
the disconnects used also segment the battery strings into lower voltages.24
The recyclability of all present and future energy storage systems needs to be accounted for, as
many materials used in energy storage systems can be toxic to the environment/humans. If
environmentally toxic materials are to be used, the researchers/developers and engineers need to
think of how this product can and will be recycled to minimize potential damage to the
environment.
4.2.2 Planning for the inevitable future abuse
The design must also consider what will happen if the user “abuses” the product. There are many
ways they can do this, and they cannot all be covered here, and can be unique to each design. As
an example, consider rechargeable ZnMnO2 batteries. This is the common alkaline chemistry that
consumers use in their AA, AAA, C, and D size cells today as primary (non-rechargeable)
batteries. Because the energy density of this chemistry is so high and the costs of production and
materials so relatively inexpensive, there is a lot of interest in trying to make this technology
“rechargeable”. The problem with any zinc-based chemistry is uneven re-plating of the Zinc
during recharge which tends to form dendritic whiskers that eventually penetrate the separator.
24
Article 480 of NFPA 70 - https://www.nfpa.org/codes-and-standards/all-codes-and-standards/list-of-codes-and-
standards?mode=code&code=70&tab=editions

20
There is a lot of ongoing work with separators, charging regimes, and other material changes to
minimize this dendritic growth and penetration. So far, it appears that using 600 cycles of this
technology is achievable without dendritic growth separator penetration provided the right
materials especially then compound separators, are used and depth of discharge is not too deep.
5 Simulation and modeling
Modeling and simulation of conventional grid resources have been well established in power
system research. They are used extensively at transmission level or Bulk Power Systems (BPS)
for grid capacity expansion planning, resource expansion planning, integrated resource planning,
and performance analysis of ESS in electrical power systems. As the grid is rapidly transforming
to become smarter, more resilient, and more renewable, modeling and simulation also needs to
expand to include new types of generation as well as energy storage resources. Although modeling
and simulating these novel resources has been studied extensively over the past few years, there is
still a lot of space to improve modeling approaches for power system impact assessments by
incorporating new controls, protection and faster response, and also to evaluate performance and
economic viability of new technologies for their rapid deployment to facilitate proper operational
paradigm shift in industry practices. One of the technical challenges pertinent to fast evolving grid
technologies includes incorporating energy storage systems in the existing model-based
framework where a model representing electro-chemical dynamics of energy storage systems is
leveraged for economic and reliability analysis. Advances in energy storage modeling for seamless
integration to grid-level economic and reliability analysis and production simulation will improve
the quality of evaluation of proposed energy storage systems, resulting in technically sound and
economically feasible energy storage projects. Figure 2 is a convenient snapshot covering the
spectrum of simulation tools that are being used to evaluate energy storage in power system and/or
resource studies in context of use cases, and physical location within the overall Generation-
Transmission-Distribution, or G-T-D, ecosystem.
All existing tools can use improvement through further R&D to explicitly represent, or model,
‘stacked applications’ in simulation studies, e.g. modeling BESS that are capable of more than one
application coincidentally at the same time, or sequentially over time.
Another challenge is modeling and simulation for capacity planning and interconnection impact
evaluation at distribution level, which is an emerging industry practice due to stochastic generation
and load, including renewables, distributed grid architecture (e.g., microgrid), behind-the-meter
generation and energy storage. Given that energy storage systems span the full spectrum of the
T&D system, advances in distribution modeling will positively impact DER energy storage,
especially, for stability and reliability of distribution systems. Also, the unbalanced nature of
distribution grid prevents using existing transmission modeling tools and requires new simulation
tool development. Further useful discussion of some of the challenges of distribution system

21
modeling including newer inverter based DER in general is provided in a recent NERC report.25
This chapter won’t address distribution planning tools and processes. The remainder of the chapter
will mainly focus on the industry area where modeling and simulation are actively used, i.e. for
BPS planning and operations.
Figure 2. Available Tools for Modeling & Simulation of Energy Storage
5.1 Modeling
The frame of reference for modeling grid energy storage spans from materials at cell level (e.g.
electro-chemical dynamics within a battery), battery level models representing physical and
electrical characteristics at the DC terminals, to system-level models of energy storage systems
representing characteristics at the point of common coupling, i.e., the interface between an energy
storage system and the receiving power grid. The time frames for reasonably valid dynamic
representation and the modeled phenomena vary with the battery application and the focus of a
particular simulation domain.
25
NERC Report: Summary of Activities BPS-Connected Inverter-Based Resources and Distributed Energy
Resources, 2019, https://www.nerc.com/comm/PC/Documents/Summary_of_Activities_BPS-
Connected_IBR_and_DER.pdf

22
IEEE has standardized representation of different synchronous machine generation technologies
for transient, dynamic, and steady state system analysis. More recently the same standard model
approach has been taken to model the different inverter technologies used for PV and wind
generation, and to some extent different wind generation technologies. There is a need to
implement standard models for EES as well. Electrochemical cell models typically include a set
of partial differential equations that precisely describe the electrochemical processes of each
battery cell. Therefore, those models provide detailed dynamics of battery cells but are too
complicated to be used in system-level analysis. Battery level models often represent the
characteristics of multiple battery cells as a whole instead of detailing each individual cell. These
types of models often assume all cells are identical and generalize cell behaviors to only represent
electrical characteristics at the DC terminals. Therefore, they are simpler than electrochemical
models, but they often lack important specific electro-chemical properties. For example,
equivalent electric circuit model in Figure 3 is one of the commonly used battery models. It
consists of dependent voltage source, which typically varies with different operating conditions
such as State of Charge (SoC) and temperature, and equivalent series resistance and capacitance
representing the battery internal ohmic losses. This circuit representation considers some of
chemical phenomenon such as diffusion processes in the cell, however, it lacks deeper physical
mechanisms of battery cells. Completely different technical issues arise when modeling flow
batteries, for instance, where other phenomena come into play and where “cells” per se are not at
issue. Models that delve deeply into the internal physical and electro-chemistry processes are
important to researchers, designers, testers but are in general not essential to the planning and
operation of battery systems except insofar as they dictate understanding of performance
degradation as a result of time, environmental factors, and usage. They are also critical to the
development of asset monitoring and maintenance analytics.
Figure 3. Equivalent electric circuit model of a battery cell
Among battery level models, empirical models which are constructed using experimental data, are
often used since these models are relatively easier to derive without domain-specific knowledge
and experience and also are capable of reflecting realistic constraints such as capacity degradation.
However, empirical models are only valid for a specific battery technology from which the
experimental cell data were collected and utilized for model identification and cannot be applicable
to broader range of battery technologies and operating conditions. Furthermore, accurate model
estimation and prediction require dealing with large experimental data set capturing spatiotemporal
electrochemical dynamics which remains as a challenging task. Hence, it is an important R&D

23
task to leverage detailed battery knowledge and experimental data to develop semi-empirical
models that are flexible to various operating conditions as well as wide range of battery
technologies. While detailed cell models will likely remain proprietary to the technology
developers there would be great value in agreeing on standard empirical models which can be used
in evaluating the performance and economics of battery systems, and which could be embedded
as needed in analytics focused on particular market and T&D applications.
At the BESS system level, an ESS is often composed of two main components including the battery
and the power conversion system. Therefore, an ESS model is often the combination of battery
and inverter models. For example, a comprehensive ESS model can be built by connecting the
circuit model above with an inverter model. Hence, the grid-connected energy storage model in
this example is essentially an inverter model which consists of DC voltage source representing
battery model and associated control systems for controlling battery as well as inverter shown in
Figure 4. There are different control configurations depending on control objectives. Control
design is different based on applications, but high-level control objectives often minimize cost,
maximize revenue, or maintain certain grid variables (line loading, voltage, etc.) within
parameters. Control design also involves providing the correct BESS response to external control
signals such as frequency regulation.
The BMS usually monitors status of the battery to maintain its high performance and safe
operation. It also interacts with the inverter control module to achieve the overall control objective
of the battery systems in the grid. The performance of the ESS largely depends on the control
design; hence it is a R&D priority to design optimal control strategies depending on grid
applications.
Figure 5 shows an example of inverter-based control model of ESS, essentially a model of a current
injection (grid following) type inverter26
and provides the “base” ESS model for BPS planning and
operation.
One important aspect of grid-connected energy storage system model is the multi timescale
applications. For dynamical stability simulations that typically have 15 second time frame, energy
26
https://www.wecc.org/Reliability/Battery%20Energy%20Storage%20Model%20Spec.pdf
Battery
Model
Inverter Grid
Inverter
Control
Battery
Control
Figure 4. Topology of inverter-based battery energy storage systems

24
limitation is generally not an issue, this could change if for instance capacitive energy storage
technology were deployed for stability enhancement and those energy storage system models can
be considered as ideal voltage or current sources. However, the dynamic performance of the battery
and its sub second response become critical. For power system techno-economic studies, such as
production cost modeling and optimal power flow which encompass multi hour timeframe, the
limited energy aspect of energy storage becomes hard constraints, hence ESS limitations needs to
be represented explicitly in the model. Development of standard models appropriate to different
time frames and applications is important, as mentioned above.
Figure 5. Example of Energy Storage System Model for Electrical Studies
Following is the example of short-term applications of energy storage systems, which is dynamic
stability power systems study, capturing the relatively fast and accurate response of modern
inverter based ESS, versus traditional generators. This modeled attribute is important for modeling
ramp-rate management of renewable resources and advances fast frequency response with tuned
modulation (i.e., synthetic inertia). Figure 6 shows an example of a modified power flow format
model that added ramp-rate management as well as synthetic-inertia capable active damping.27
27
https://www.archive.ece.cmu.edu/~electriconf/2011/pdfs/A123-CMUElectricityIndustry-09MAR2011-v2.pdf
Ramp Rate
Management
Frequency
Droop
Voltage
Regulation

25
For models used in production simulation, and economic analysis in general, model representation
is typically unconcerned with dynamic behavior but is concerned with state of charge and
Megawatt hour (MWh) duration modeling, modeling losses as a function of charging/discharging
rate, modeling self-discharge losses where applicable, and modeling performance degradation as
a function of usage and duty cycles. The latter turns out to be critically important in understanding
the economics and design of BESS deployed for frequency regulation, time arbitrage, and peak
shaving applications where frequent cycling is the norm. Limits on depth of discharge are the
norm for high duty cycle applications, and battery capacity oversizing to allow for long term
degradation are the norm in many T&D applications within daily duty cycles. These can have
startlingly large impacts on total system costs and economics so understanding them well is
critical.
5.2 Simulation
The model examples above provide a useful basis for discussing the simulations that include
energy storage in power systems and resource expansion and impact studies, and more importantly,
help identify modeling and simulation limitations that R&D can address.
5.2.1 Dynamic simulation study examples
As an example, the dynamic stability simulation, mentioned in the previous section, can be used
in interconnection study and interconnection approval requirement to demonstrate that combined
wind generation and BESS output could meet a defined maximum ramp rate at the point of
common coupling. Figure 7 shows an example output from a dynamic simulation study that used
the models shown in Figure 5 above.28
28
https://www.archive.ece.cmu.edu/~electriconf/2011/pdfs/A123-CMUElectricityIndustry-09MAR2011-v2.pdf
Figure 6. Example of Energy Storage System Model for Dynamic Simulation Studies

26
Figure 7. Example of Energy Storage System Model for ESS Interconnection Dynamic Stability Study
The grid-level and utility-level impact studies of ESS integration based on simulation can prove
the economic viability of ESS-grid interconnection before real grid-scale demonstration and are
essential for interconnection project approval. Hence, the simulation studies are critical path
requirements for the project. More recently, an example of use of dynamic simulation to explore
the value of energy storage for wide area stability was performed by Sandia National Laboratory
(SNL) and the Bonneville Power Administration (BPA). In this case, dynamic simulation was used
to show that 100MW of ESS can effectively damp sub-synchronous resonant frequency modes on
the Western Interconnect. Figure 8 shows an example from the project reporting.29
The inclusion of ESS dynamic models and simulation studies is now a common industry practice
in any transmission interconnection projects. However, using ESS modeling and simulation for
evaluating impacts of ESS in large-scale interconnected grids for operation and planning purposes
is still computationally and algorithmically challenging and is not yet common practice. Further
R&D to refine the ESS models to fit this more general studies framework is needed.
Modeling frequency rate-of-change response by generators is important in system simulations. It
applies to the electromechanical dynamics of how generator rotor angles with respect to the local
grid change as a function of sub-cycle changes in power output due to machine inertia, excitors,
and grid electrical behavior including all synchronous machines. This approach, using simple
control schemes and representing faster than real time response, is not realistic for a single ESS,
as it requires analysis on a system basis to properly respond to frequency changes.
29
https://www.sandia.gov/essssl/docs/pr_conferences/2014/Friday/Session10/03_Schoenwald_Damping_Control.
pdf

27
Figure 8. Example of Wide Area Dynamic Stability Simulation to Evaluate ES Impact
In the future, use of high-speed PMU data may permit something akin to a fast frequency response
on an autonomous basis. This may be an interesting R&D topic.
5.3 Economic study examples
Early economic models of BESS used for frequency regulation simply calculated the expected
revenues in frequency regulation markets of particular ISOs against the capital and operating costs
of the BESS and any expected energy losses. Figure 9 shows an example of a long-term ESS
economic analysis30
. Development of such models was attractive as the frequency regulation
market has the highest value and is most accessible to BESS developers, especially following
FERC Order 755. All required data was publicly available (ISO ancillary market historical prices
and historical AGC frequency regulation signals) and the analysis was fairly straightforward.
Improved versions of this analysis are readily available today from Sandia, EPRI, DNVGL, Quanta
Technology, and other sources. For example, several years after the work behind Figure 9 was
performed, a detailed application of storage valuation and operations analytics after the fact to
validate early SDG&E storage projects was performed. This validated pre-commercial versions
of these analytics for a variety of storage T&D applications with stacked benefits.31
Figure 9
shows an illustration of the best-case dispatch of a large BESS project used for substation capacity
deferral and with stacked applications for participation in day ahead energy, balancing/real time
energy, and regulation markets. Today these “pre-commercial” analytics are available as software
30
Energy Storage Cost‐effectiveness Methodology and Results, DNV KEMA Energy & Sustainability, August 2013;
https://ww2.energy.ca.gov/2014publications/CEC-500-2014-068/CEC-500-2014-068.pdf
31
SDG&E and Quanta Technology, EPIC-1 Project 5 Part 2 Pre-Commercial Demonstration of Methodologies and
Tools for Energy Storage Integration into Distribution Circuits, 2017

28
tools for use by utility planning departments considering storage for the comprehensive list of
applications described above.
Figure 9. Example of ESS Model Results Used for an Economic Assessment
As the ISO community developed different fast regulation market products in response to FERC
755 it became critical to understand the tradeoffs among battery storage duration, fast regulation
control algorithm design, battery degradation with duty cycling, and “pay for performance”
regulation tariffs/settlements. The available tools for assessing frequency regulation application
became more and more sophisticated. This is an example of an instance where market demand
sufficed to drive the research needed on a pragmatic and commercial basis.
One of the challenges for existing economic evaluation tools is increased temporal resolution
required for the analysis. Available tools had a time resolution of 1-hour increments, driven by
the hourly time frame of the old generation and ancillary markets, and scheduling. However,
hourly analysis is inadequate to capture the BESS performance constraints that may limit the BESS
operation in response to control signals and is not appropriate for analyzing BESS participation in
real time energy markets with 5-minute market clearing and dispatch. Thus, long-term economic
analysis requires a particular assumptions or approximations corresponding to each use case to
reduce computational burden, or on a combined high time granularity analysis for some periods
and hourly analysis for a full year. These approaches have proved sufficient to analyzing BESS
for today’s market and bulk energy system applications. If use of inertial response from BESS is
adopted these approaches will have to be revisited.
Modelling an individual ESS in a single ancillary or energy market is routine today. Analyzing a
portfolio of ESS in a co-optimized market is a significantly more complex problem and one for
which complete solutions do not exist today. Without going into great detail, introducing critical
ESS model elements (state of charge limits, losses, degradation, minimum rates of
charge/discharge) into the Mixed Integer Programming (MIP) market solutions used at all the ISOs
and in all commercially used production cost simulations creates complexities and challenges to
reliable solutions and computational time. Complicating factors include the introduction of

29
additional integer variables for each ESS, which adversely affects computation time; and the
creation of “flat” objective functions (low sensitivity to decision variable changes) which can
affect convergence. These issues are expected to become more and more important as the shift to
near-zero marginal cost renewable resources increase and as the number of ESS in the markets
increases.
In the bulk power space, meaning markets, production cost simulation, and load flow/contingency
analysis, the necessary data is generally available to all parties with legitimate interest (e.g.
transmission companies, storage and generation developers, market analysts and traders, and
interveners.) Consensus models of the transmission system and generation resources are readily
available as part of licensed commercial production costing software products. They are generally
accepted in the financial community as the basis for evaluating planned projects. The mathematics
and algorithms are well understood, if somewhat limited in their ability to handle significant
numbers of storage resources. These conditions are definitely not the case in the distribution space.
Analyzing the applications of energy storage on distribution systems is increasingly important both
in terms of accommodating higher penetration of distributed energy (solar) resources and also as
a response to the Non-Wires Alternative initiatives underway in many states today. Exchanging
distribution system electrical models with other utilities much less with third party developers,
intervenors, or potential market participants has never been an accepted practice. Unlike the bulk
power space, the use of IEEE standard models is not common due to the lack of need for data
exchange. Consequently, while the theoretical models for distribution analytics may be common,
the data representations in different widely used distribution analytics are non-standard and
converting data from one tool’s format to another is not well supported by many mainstream tools.
Distribution circuit models today originate with equipment representation in Geographic
Information System (GIS) data bases. The representation of common apparatus in one utility’s
GIS implementation may be radically different from another utility’s implementation, even with
the same GIS software. Apparatus represented in these data bases may not be germane to circuit
node-branch representations (so called “zero impedance links” representing fuses, switchgear, bus
bars, and the like). Distribution analytics use a radial circuit solution algorithm that can handle
these elements. More sophisticated tools such as optimal power flows require node-branch
representations akin to transmission models, so some conversion effort is inevitable.
Most storage applications require time series simulation across the hours in a year, or a subset of
those hours. Distribution planning has historically been concerned with the annual peak load hour
(for capacity planning) and today increasingly with shoulder hour analysis (peak PV production,
minimal load for PV hosting capacity analysis). Usable time series data is generally not available
and time series analysis is uncommon. In distribution circuits, routine switching operations,
changing which phase a load is connected to, normal local outages, - all cause discrepancies in
time series data that have to be removed.
In almost all bulk power production costing simulations and ancillary market simulations, a full
AC representation of the transmission system is not required, and DC models are the norm. This

30
is critical to computational feasibility and time. In the distribution space, maintaining voltage
levels within limits is central to capacity and reliability planning, and to avoiding power quality
issues. So, a full AC analysis is mandatory, which complicates the problem of storage co-
optimization across time and applications.
None of these issues of analyzing storage in distribution require basic research, they are more of
the nature of software development and integration block and tackling. However, optimizing
storage location, sizing, and dispatch increases the complexity of analytics by an order of
magnitude.
The lack of easy access to data, familiarity with the commercial planning tools, and the complex
nature of the problem is probably a reason that “public domain” storage valuation tools do not
support integration with distribution planning tools and analysis of distribution applications as well
as they do bulk power and behind the meter applications. Work in this space has typically been
performed by utilities and support organizations that routinely work with utilities around
distribution planning problems. As an example, the Energy Storage Planning Methodology and
Tool, shown in Figure 10, analyzes and plans storage for a variety of T&D applications, integrated
with commercial planning tools.32
Figure 10. Energy Storage Planning Methodology and Tool
Key aspects of energy storage planning include forecasting load and DER, time series analysis
(e.g. hourly), siting and sizing optimization, and techno-economic valuations. The approach is
built on detailed analysis using industry load flow models (that differ for T and for D applications),
coupled with methodologies that help identify sites and sizes for each application, quantify the
potential stacked revenues from wholesale market participation, and finally a techno-economic
model for cost-benefit analysis. Some applications require the quantification of system wide
impact such as congestion relief, that require analysis using production cost models.
32
D. Novosel, “Technology Solutions for Evolving Energy Industry,” IEEE PES ISGT, Washington DC, February 2019.

31
Additionally, one of the future challenges, especially in spatiotemporal simulation, are the
uncertainty aspects of modern power systems due to increasingly unpredictable and variable
generating resources and loads. Uncertainty in modeling and simulation is especially challenging
due to stochastic nature of these problems. For example, the number of cases to consider in a
simulation increases exponentially with the number of uncertain sources in the grid. Monte Carlo
approaches are commonly used for this purpose, but these are computationally burdensome, and
sensitivity analyses or probabilistic approaches33
are widely used as alternatives to repeated
simulations. Uncertainty quantification for stochastic power system operation and planning is
relatively well studied34
, but it is still challenging for large-scale power systems. Hence, it is one
of the important R&D areas to achieve accurate and robust techno-economic analysis and planning
of a grid with energy storage systems.
6 Technology Gaps and Future Needs
6.1 Introduction
Energy storage is beginning to enable convergence across key areas with significant potential to
affect the future of the electricity industry. These include rapid growth of renewables, initiatives
by state and local bodies pursuing clean energy technologies, electrification of transportation, and
the growing recognition of the need to assure grid reliability and resilience through modernization
of the electric grid. Large scale integration of energy storage in the electric grid infrastructure will
certainly have a transformative effect. Energy storage will provide numerous benefits that have
bearing on how the future grid operates, providing grid operators with a flexible asset that can
respond to situations that could not be handled in the past.
Energy storage will play a major role in integrating renewables. The amount and type of energy
storage solutions needed for renewable firming continues to evolve, making even more obvious
the significant gaps in what current energy storage technologies can provide. As states and cities
continue to push towards higher renewable targets, with many states moving towards 100% clean
energy, the need for low cost energy storage – including long duration and seasonal storage –
becomes ever more important.
Recent major changes in the standards for interconnection such as IEEE 1547-2018, IEEE 1547.1-
2020, and UL 1741 with the electric power system will become mandatory for all DER equipment
by 2021. These will likely increase demand for energy storage as a part of DERs. Specific technical
improvements embedded in these standards are: DER voltage regulation and ride through,
interoperability, discussion of islanding and microgrid, and a set of rigorous testing and
verification requirements. These changes address the industry concerns for increased use of
33
https://www.sciencedirect.com/science/article/pii/S1364032118307317
34
https://www.pnnl.gov/main/publications/external/technical_reports/pnnl-23680.pdf

32
inverter-based DER when they represent a large proportion of energy sources on a utility
distribution circuit.
The addition of interoperability requirements will allow energy storage systems to produce more
value by seamlessly communicating with ISO and utility control systems used today to control
conventional resources for market and grid operations such as Market Management Systems
(MMS) , Energy Management Systems (EMS), SCADA, Advanced Distribution Management
Systems (ADMS). Increased interactive distributed generation allows the optimization of these
resources and improves efficiency and availability of electricity production and delivery.
Except for pumped hydro, modular energy storage based on batteries and other technologies is
new in the electricity infrastructure. Markets for energy storage are new and the amount of modular
storage currently installed is modest, especially when compared to the needs of the future grid.
Effective integration of energy storage across the electricity infrastructure requires significant
technical advances in a number of areas including system integration, engineering of ESS that are
safe and reliable, improved operational performance to make energy storage cost-effective across
application markets. While some technical gaps can be fixed with additional engineering and
deployment, others will require significant further research and development.
The following sections highlight some of the technology gaps and describe future R&D needs for
energy storage to become ubiquitous in the electricity infrastructure.
6.2 Engineering and integration of Energy Storage Systems (ESS)
6.2.1 Systems engineering
ESS incorporate not only of devices for storing energy in various forms but also include a high
degree of integration of power converters, system level optimization and control architectures, all
put together to provide valuable services to the grid operators. So far, there has been minimal effort
at bringing synergies to integration and balance of plant issues. Engineering ESS is still an art.
Further R&D is needed for engineering ESS at scale ranging from behind the meter storage to
large grid-connected energy storage plants. There is also a need for higher level of integration of
distributed controls and sensors to seamlessly manage bidirectional power flow. Integration of
energy storage in distribution and transmission operations is at an early stage and there is a need
for energy management systems for system control and dispatch. Engineering practice related to
balance of system needs significant amount of development to realize lower costs and system
reliability. With large scale integration of energy storage and DER, operations based on power
electronic conversion will become pervasive. So far, cost reductions in power electronics have
been slow and there is a need for significant advances in modular power converter architectures to
reduce system complexity and improve balance of system cost.
6.2.2 Energy storage integration
Integration is the way that storage will support the overall grid and its customers. There is a host
of challenging issues that need to be addressed. Some of these issues are summarized below.

33
Effective system integration is a challenging problem for energy storage due to the great diversity
of potential applications, each of which has its own set of constraints and performance
requirements. Over the next decade, the diversity of energy storage installations will expand in the
range of applications, in size and scale, and in system complexity. As energy storage gets
integrated at the generation, and in transmission and distribution systems, installations with higher
power capacities and higher working voltages will be needed, along with the need to streamline
engineering to hybridize and co-optimize energy storage with renewable and distributed resources.
To enable these developments, flexible and modular power conversion solutions are needed.
System integration must ensure that the needs of the application, grid environment, and storage
devices are simultaneously met. The power conversion system provides the physical connection
between storage resources and the grid and is the sole actuator responsible for accomplishing
application-specific power flow control functions. Consequently, the power conversion system is
a dominant aspect on the system integration problem, and the power conversion system’s
limitations drive the integration challenges encountered in battery management, system protection,
and balance of system.
There is, at present, an accepted conventional structure for power conversion systems in BESS
applications. In almost all cases, a voltage source inverter connects directly to DC terminals of the
energy storage system and converts DC to AC in a single stage. At the AC interface, an isolation
transformer matches the inverter output voltage to the voltage at the desired point of connection.
This is the simplest possible structure capable of connecting DC storage devices to the grid, and
its simplicity is the reason for its pervasiveness. The simplicity of this single-stage topology is
advantageous in many ways, but it offers very little flexibility. Specifically, this structure couples
storage system design constraints to the AC voltage level: the minimum DC voltage must be
greater than the peak line-to-line voltage at the inverter output or its ability to effectively control
power flow will be lost. This voltage constraint creates scalability problems. In grid applications,
moving to higher working voltages is a universally preferred strategy for achieving higher power
capacity due to the minimization of ohmic losses. However, if applied to energy storage systems
with single-stage power conversion structures, it places impossible minimum DC voltage
constraints on the energy storage devices. This is especially problematic as single cell voltages are
lower than 5V.
6.2.3 Modularity and power conversion systems
To keep pace with the expanding scope of storage applications, flexible power conversion
structures are needed. Modular converter topologies constructed from highly optimized power
electronic building blocks are widely recognized as an effective strategy for enabling flexible
utility-scale power conversion systems. Modular structures make it possible to develop both high-
power converters for direct medium voltage grid connection and low voltage distribution-level
conversion systems using the same standard set of tools. Within a modular design framework,
redundant converter modules can be used to improve system fault-tolerance and increase overall
reliability. Once operational, modular structures minimize downtime due to converter failures by
reducing repair efforts to module replacement from a standard stock.

34
For circuit designers and system integrators, modularization provide the tools necessary to produce
power conversion solutions that meet the needs of next-generation energy storage systems. For
owners and operators, these modular power converters provide operational benefits that align with
the needs of effective utility asset management. While advantages of power converter topologies
are compelling, further research is needed to develop modular topologies that optimally match the
needs of storage applications and to identify optimal internal module configurations.
At the system level, the impact of modular topologies on the integration challenges of energy
storage applications needs to be quantified. As an example, modular structures may be used to
eliminate the minimum DC voltage constraint described above and allow multiple low-voltage
storage systems to interface directly with medium voltage grid connections. This is a fundamental
change for system integration, and has significant implications for system protection, battery
management, and balance of system costs. Quantitative analyses of the advantages and
disadvantages of modular configurations over conventional system structures are still limited.
These analyses, along with supporting data from full scale demonstrations, are necessary to
determine optimal solutions from within the set of possible system structures enabled by power
conversion system modularization.
The consequences of keeping batteries, especially systems based on Li-ion batteries, connected to
power electronics for the life (10+ years) of the installation may need to be better understood. For
example, lead-acid batteries are robust and can absorb DC ripples, though they occasionally fail
when left on float charge for long periods of time. On the other hand, Li-ion cells are more sensitive
to ripple currents. With a large number of suppliers in the power converter market, there is a need
to better understand the acceptable DC switching noise amplitudes and frequencies. In addition,
unexpected secondary reactions may take place, e.g. a DC ripple can cause enough pulsing
capacitance to create a mechanical vibration that can lead (over the years) to mechanical failures,
for example, of tab welds.
Research challenges within a power electronics module involve integration of power stage
components, control devices, and supporting electronic circuitry for maximum conversion
performance, electromagnetic compatibility, and operational flexibility. New components, such as
wide bandgap semiconductors and advanced passives, increase power processing capabilities and
enable new system integration strategies. Modular topologies do not reduce the need for material
and component R&D; modularization provides a standard platform to focus component
development efforts. The same applies to advances in thermal management and component
packaging, which are cross-cutting needs in power conversion applications. Finally, there is a need
for standardization of communication between modules and other system elements, and standard
mechanisms for ensuring secure transfer of information within communication channels.
6.2.4 Renewable integration with ESS
With increasing renewable generation, there is a need to optimize the operation of renewables with
energy storage. During the last ten years, renewables have been integrated to a large degree, albeit,
at a lower level of penetration. The question is what will happen at high levels of penetration in a
system that includes energy storage. There is still need to advance knowledge and technology to

35
improve planning and operations. In the case of operations, it is not clear if it is sufficiently well
known how the dynamics of the power grid will change with large scale integration of inverter-
based assets such as renewables and energy storage. There is also a control aspect based on forecast
and co-operation of renewables plus storage.
In order to support economic projections of performance of ESS, there is a need to advance
knowledge and methods for providing accurate estimates of operation of energy storage integrated
with renewables resources over their entire life cycle. It is necessary to obtain high fidelity,
scalable dynamic models for energy storage systems and other distributed inverter-based systems
to assist operations and planning studies. It is necessary to improve methods for forecasting and
co-operation of renewables plus storage that are capable of minimizing uncertainty-related risks
and maximizing the benefits from energy storage assets. Methods for developing and deploying
patches in industrial control systems software/firmware are a research gap. This is a known
problem for all connected systems.
6.2.5 Interoperability and cyber security
As with all distributed energy resources, energy storage systems, especially battery energy storage
systems have a potential to be affected by cyber-attacks. Defining cybersecurity requirements and
threat models for energy storage systems is still a topic that generates confusion and must be
streamlined. If optimal usage of energy storage systems in as many applications as possible is to
be achieved, secure, low-latency, reliable and low-cost communication systems should be
deployed to allow interoperability. However, using dedicated communication systems might not
be a feasible solution in a scenario of pervasive distributed energy storage system. Furthermore,
some applications might require centralizing and processing large amounts of data. Therefore, it
is necessary to develop standards and methods for enabling secure communications between
energy storage systems and utilities or aggregators, over private or public infrastructure, that are
effective at very large scale. Furthermore, it is necessary to research methods for developing and
deploying software patches for industrial control systems, such as energy storage, that can fix
known vulnerabilities while respecting reliability and continuity of service constraints of power
systems assets.
Today, cybersecurity is mostly centered around the use of passwords, some with two-factor
authentication, along with physical access restrictions. There is still considerable room for
improvement and cybersecurity for DERs remains an open topic of discussion. IEEE 1547-2018
recognized that cybersecurity is important but deferred that topic to other standards. IEEE 1547.3
is presently in the process of being revised, and this standard will include cybersecurity in more
detail.
The internet industry has created strong standards for security that are a starting point for grid
communication networks. However, there are unique challenges faced by energy storage systems.
A denial of service attack only has to slow down controls enough to make them slower than the
fastest characteristic time of the system to create a potentially unsafe situation. Therefore, energy
storage systems need to maintain the ability to safely return to nominal conditions, even in the
absence of digital controls.

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