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Electrical energy storage

Electrical Energy Storage (EES) is the process of converting electrical energy from a power network into a form that can be stored for converting back to electricity when needed. EES enables electricity to be produced during times of either low demand, low generation cost, or during periods of peak renewable energy generation. This allows producers and transmission system operators (TSOs) the ability to leverage and balance the variance in supply/demand and generation costs by using stored electricity at times of high demand, high generation cost, and/or low generation capacity.
EES has many applications including renewables integration, ancillary services, and electrical grid support. This blog series aims to provide the reader with four aspects of EES:

  1. An overview of the function and applications of EES technologies,
  2. State-of-the-art breakdown of key EES markets in the European Union,
  3. A discussion on the future of these EES markets, and
  4. Applications (Service Uses) of EES.

Table: Some common service uses of EES technologies

Storage Category

Storage Technology

Pumped Hydro

Open Loop

Closed Loop

Electro-chemical

Batteries

Flow Batteries

Capacitors

Thermal Storage

 

Molten Salts

Heat

Ice

Chilled Water

Electro-mechanical

Compressed Air Energy Storage

Flywheel

Gravitational Storage

Hydrogen Storage

 

Fuel Cells

H2 Storage

Power-to-Gas

Unlike any other commodities market, electricity-generating industries typically have little or no storage capabilities. Electricity must be used precisely when it is produced, with grid operators constantly balancing electrical supply and demand. With an ever-increasing market share of intermittent renewable energy sources the balancing act is becoming increasingly complex.

While EES is most often touted for its ability to help minimize supply fluctuations by storing electricity produced during periods of peak renewable energy generation, there are many other applications. EES is vital to the safe, reliable operation of the electricity grid by supporting key ancillary services and electrical grid reliability functions. This is often overlooked for the ability to help facilitate renewable energy integration. EES is applicable in all of the major areas of the electricity grid (generation, transmission & distribution, and end user services). A few of the most prevalent service uses are outlined in the Table above. Further explanation on service use/cases will be provide later in this blog, including comprehensive list of EES applications.

Area

Service
Use/Case

Discharge
Duration in h

Capacity
in MW

Examples

Generation

Bulk
Storage

4
– 6

1
– 500

PHS,
CAES, Batteries

Contingency

1
– 2

1
– 500

PHS,
CAES, Batteries

Black
Start

NA

NA

Batteries

Renewables
Firming

2
– 4

1
– 500

PHS,
CAES, Batteries

Transmission
& Distribution

Frequency
& Voltage Support

0.25
– 1

1
– 10

Flywheels,
Capacitors

Transmission
Support

2
– 5 sec

10
– 100

Flywheels,
Capacitors

On-site
Power

8
– 16

1.5
kW – 5 kW

Batteries

Asset
Deferral

3
– 6

0.25
– 5

Batteries

End
User Services

Energy
Management

4
– 6

1
kW – 1 MW

Residential
storage

(Jon Martin, 2019)

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Machine learning makes smarter batteries

Renewable energies, such as wind and solar energy are naturally intermittent. To balance their demand and supply, batteries of, for example, electric vehicles can be charged and act as an energy buffer for the power grid. Cars spend most of their time idle and could, at the same time, feed their electricity back into the grid. While this is still a dream of the future, commercialization of electric and hybrid vehicles is already creating a growing demand for long-lasting batteries, both for driving as well as grid buffering. Consequently, methods for evaluating the state of the battery will become increasingly important.

The long duration of battery health tests is a problem, hindering the rapid development of new batteries. Better battery life forcasting methods are therefore urgently needed but are extremely difficult to develop. Now, Severson and her colleagues report in the journal Nature Energy that machine learning can help to predict computer battery life by creating computer models. The published algorithms use data from early-stage charge and discharge cycles.

Normally, a figure of merit describes the health of a battery. It quantifies the ability of the battery to store energy relative to its original state. The health status is 100% when the battery is new and decreases with time. This is similar to the state of charge of a battery. Estimating the state of charge of a battery is, in turn, important to ensure safe and correct use. However, there is no consensus in the industry and science as to what exactly a battery’s health status is or how it should be determined.

The state of health of a battery reflects two signs of aging: progressive capacity decline and impedance increase (another measure of electrical resistance). Estimates of the state of charge of a battery must therefore take into account both the drop in capacity and the increase in impedance.

Lithium ion batteries, however, are complex systems in which both capacity fade and impedance increase are caused by multiple interacting processes. Most of these processes cannot be studied independently since they often occur in simultaneously. The state of health can therefore not be determined from a single direct measurement. Conventional health assessment methods include examining the interactions between the electrodes of a battery. Since such methods often intervene directly in the system “battery”, they make the battery useless, which is hardly desired.

A battery’s health status can also be determined in less invasive ways, for example using adaptive models and experimental techniques. Adaptive models learn from recorded battery performance data and adjust themselves. They are useful if system-specific battery information are not available. Such models are suitable for the diagnosis of aging processes. The main problem, however, is that they must be trained with experimental data before they can be used to determine the current capacity of a battery.

Experimental techniques are used to evaluate certain physical processes and failure mechanisms. This allows the rate of future capacity loss to be estimated. Unfortunately, these methods can not detect any intermittent errors. Alternative techniques use the rate of voltage or capacitance change (rather than raw voltage and current data). In order to accelerate the development of battery technology, further methods need to be found which can accurately predict the life of the batteries.

Severson and her colleagues have created a comprehensive data set that includes the performance data of 124 commercial lithium-ion batteries during their charge and discharge cycles. The authors used a variety of rapid charging conditions with identical discharge conditions. This method caused a change of the battery lives. The data covered a wide range of 150 to 2,300 cycles.

The researchers then used machine learning algorithms to analyze the data, creating models that can reliably predict battery life. After the first 100 cycles of each experimentally characterized battery their model already showed clear signs of a capacity fade. The best model could predict the lifetime of about 91% data sets studied in the study. Using the first five cycles, batteries could be classified into categories with short (<550 cycles) or long lifetimes.

The researchers’ work shows that data-driven modeling using machine learning allows forecasting the state of health of lithium-ion batteries. The models can identify aging processes that do not otherwise apparent in capacity data during early cycles. Accordingly, the new approach complements the previous predictive models. But at Frontis Energy, we also see the ability to combine generated data with models that predict the behavior of other complex dynamic systems.

(Photo: Wikipedia)

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A Durable Aluminum-Air-Battery

Non-rechargeable batteries, which depend on a reaction between aluminum and oxygen, can store significantly more energy than conventional lithium-ion batteries. The biggest limitation of such aluminum-air batteries is their short shelf life. An improved battery design could help eliminate this limitation. Aluminum and air batteries are based on the property of aluminum to corrode, which is also their weak spot:

4 Al + 3 O2 + 6H2O → 4 Al (OH)3

While an aluminum-air battery is not used, its electrodes corrode causing unwanted discharge. This self-discharge drastically shortens the shelf life of the battery. Brandon Hopkins, of the Massachusetts Institute of Technology in Cambridge, and his colleagues developed an aluminum-air battery that uses a conventional electrolyte during operation. When stored, however, the electrolyte is replaced by oil. Their article was recently published in the journal Science.

The new battery reaches a storage capacity of almost 900 Wh / kg. This makes the prototype comparable to other aluminum-air batteries. In contrast, the new corrosion protection extends the storage time 10,000-fold. The authors suggest that such a battery could be used in long-range drones and grid-independent power generation. At Frontis Energy, we believe that batteries with high storage capacity and durability can be used almost anywhere, for example for sensors and other applications.

(Photo: George Hodan)

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Cobalt Nanocrystals Make Lithium-Ion Batteries Age More Slowly

In todays Li-ion batteries, cobalt oxide cathodes improve performance and durability. While, such cobalt cathodes show the same performance as nickel oxide cathodes, they come at a higher price. Nickel cathodes, in turn, crack and dissolve quickly, which reduces their lifespan. Nevertheless, nickel cathodes are very popular because they are so cheap.

Now, the research team led by Jaephil Cho of the Ulsan National Institute of Science and Technology in South Korea has developed a cathode made of more than 80% nickel. The researchers reported in the journal Energy & Environmental Science that a cathode coated with nanocrystals of cobalt aged more slowly than conventional nickel cathodes. After recharging 400 times at room temperature, the battery was able to retain 86% of its original capacity.

The novel nickel cathodes could help meet the growing demand for rechargeable batteries in electric vehicles if cobalt prices rise in the future.

(Photo: Wikipedia)