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Energy storage in the European Union

Grid integration of renewables

In our previous post of this blog series on Electrical Energy Storage in the EU we briefly introduced you to different technologies and their use cases. Here, we give you a short overview over the EU energy grid.  Supplying approximately 2,500 TWh annually to 450 million customers across 24 countries, the synchronous interconnected system of Continental Europe (“the Grid”) is the largest interconnected power network in the world. The Grid is made up of transmission system operators (TSOs) from 24 countries stretching from Greece to the Iberic Peninsula in the south, Denmark and Poland in the north, and up to the black sea in the east. The European Network of Transmission System Operators (ENTSO-E) serves as the central agency tasked with promoting cooperation between the TSOs from the member countries in the Grid. The ENTSO-E, in essence, acts as the central TSO for Europe. With over 140 GW of installed wind and solar PV capacity, the EU trails behind only China in installed capacity. A breakdown of the individual contributions of EU member states is shown below in the figure above.

Energy Storage in the EU

For this study a number of European countries were selected for more detailed investigation into energy storage needs. These countries were selected based on a combination of existing market size, intentions for growth in non-dispatchable renewable energy and/or energy storage, and markets with a track record of innovation in the energy sector.

On a total capacity basis (installed and planned MW) the top three energy storage markets within the EU are: Italy, the UK, and Germany. These countries were selected on the basis of these existing market sizes.

Spain and Denmark were selected based on their large amounts of existing renewable energy capacity and − in the case of Denmark − the forecasted growth in renewable energy and energy storage capacity.

While still lagging behind the rest of the EU in terms of decarbonization efforts and having a small portion of their energy from renewable sources, the Netherlands were also selected for further investigation.

Each of the selected countries (Germany, UK, Italy, Spain, Denmark, Netherlands) are discussed in the proceeding sections, providing a more detailed overview outlining their current electricity portfolios and decarbonization efforts, current energy storage statistics, and a brief discussion on market outlook.

Pumped Hydro Storage

With over 183 GW of installed capacity worldwide, pumped hydro storage is the most widely implemented and most established form of energy storage in the world. Due its extensive market penetration, technology maturity, and the fact that this blog is aimed at emerging new storage technologies, the data presented in the following posts excludes this technology.

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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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EU market summary for energy storage

Electrical energy storage (EES) is not only a vital component in the reliable operation of modern electrical grids, but also a focal point of the global renewable energy transition. It has been often suggested that EES technologies could be the missing piece to eliminating the technical hurdles facing the implementation of intermittent renewable energy sources. In the following blog posts, selected EES markets within the European Union will be evaluated in detail.

With over 80 MW of installed wind and solar capacity, Germany is by far the leading EU nation in the renewable energy transition. However, experts have argued that Germany’s need for widespread industrial scale energy storage is unlikely to materialize in any significant quantity for up to 20-years. This is due to a number of factors. Germany’s geographic location and abundance of connections to neighbouring power grids makes exporting any electricity fluctuations relatively easy. Additionally, when Germany reaches its 2020 targets for wind and solar capacity (46 GW and 52 GW, respectively) the supply at a given time would generally not exceed 55 GW. Nearly all of this would be consumed domestically, with no/little need for storage.

When evaluating energy storage in the UK, a different story emerges. Being an isolated island nation there is considerably more focus on energy independence to go along with their low-carbon energy goals. However, the existing regulatory environment is cumbersome, and poses barriers significant enough to substantially inhibit the transition to a low-carbon energy sector – including EES. The UK government has acknowledged the existence of regulatory barriers and pledged to address them. As part of this effort, a restructuring of their power market to a capacity-based market is already underway. The outlook for EES in the UK is promising, there is considerable pressure from not only industry, but also the public and the government to continue developing EES facilities at industrial scale.

Italy, once heavily hydro-powered, has grown to rely on natural gas, coal, and oil for 50% of it’s electricity (gas representing 34% alone). The introduction of a solar FIT in 2005 lead to significant growth in the solar industry (Italy now ranks 2nd in per capita solar capacity globally) before the program ended in July 2014. In recent years there has been notable growth in electro-chemical EES capacity (~84 MW installed), primarily driven by a single large-scale project by TERNA, Italy’s transmission system operator (TSO). This capacity has made Italy the leader in EES capacity in the EU, however the market is to-date dominated by the large TSOs.

However, the combination of a reliance on imported natural gas, over 500,000 PV systems no longer collecting FIT premiums, and increasing electricity rates presents a unique market opportunity for residential power-to-gas in Italy.
Denmark is aggressively pursing a 100-percent renewable target for all sectors by 2050. While there is still no official roadmap policy on how they will get there, they have essentially narrowed it down to one of two scenario: a biomass-based scenario, or a wind + hydrogen based scenario. Under the hydrogen-based scenario there would be widespread investment to expand wind capacity and couple this capacity with hydrogen power-to-gas systems for bulk energy storage. With the Danish expertise and embodied investment in wind energy, one would expect that the future Danish energy system would be build around this strength, and hence require significant power-to-gas investment.

The renewable energy industry in Spain has completed stagnated due to retroactive policy changes and taxes on consumption of solar generated electricity introduced in 2015. The implementation of the Royal Decree 900/2015 on self-consumption has rendered PV systems unprofitable, and added additional fees and taxes for the use of EES devices. No evidence was found to suggest a market for energy storage will materialize in Spain in the near future.

The final country investigated was the Netherlands, which has been criticized by the EU for its lack of progress on renewable energy targets. With only 10% of Dutch electricity coming from renewable sources, there is currently little demand for large-scale EES. While the Netherlands may be lagging behind on renewable electricity targets, they have been a leader in EV penetration; a trend that will continue and see 1-million EVs on Dutch roads by 2025. In parallel with the EV growth, there has been a large surge in sub-100kW Li-ion installations for storing energy at electric vehicle (EV) charging stations. It is expected that these applications will continue to be the primary focus of EES in the Netherlands.

Similar to Italy, the Dutch rely heavily on natural gas for energy within their homes. This fact, coupled with an ever-increasing focus on energy independent and efficient houses could make the Netherlands a prime market for residential power-to-gas technologies.

Read more about electrical energy storage here.

Jon Martin, 2019

(Photo: NASA)

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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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Wind Energy

Wind energy is short for the conversion of energy captured from wind to electrical or mechanical energy. Wind power turbines produce electrical energy and windmills produce mechanical energy. Other forms for wind energy conversion are wind pumps which use wind energy to pump water or sails which drive sail boats.

The cheapest US energy prices by source and county, Source: Energy Institute, University of Texas Austin

Since its first use on sail boats, wind energy is wide spread. Windmills have been used for more than 2,000 years as source of mechanical energy. The Scotsman James Blythe was the first who demonstrated the transformation of wind energy into electrical energy. As wind energy is a renewable source of energy, electrical energy generated by wind turbines is a clean and sustainable form of energy. Wind energy is often also cheaper than natural gas, for example throughout the entire American Midwest, as shown by the Energy Institute of University of Texas, Austin. It is therefore not surprising that wind energy is one of the fastest growing markets in the renewable energy sector worldwide. In 2015, 38% of all renewable energy in the United States and the European Union was generated by wind turbines.

Wind and solar energy production in the US and Canada in 2015. Sources: EIA, Statistics Canada

More efficient than single wind turbines is the use of wind parks where clusters of large turbines constantly generate electrical power. There are two kinds of wind parks, on-shore and off-shore wind parks. Off-shore wind parks are often more expensive but do not use valuable farmland as it is often the case for on-shore wind parks. However, wind parks on farmland can be a valuable addition for farmers seeking an extra income.

Wind and solar energy production in the European Union and the Euro-zone in 2015. WSH is the fraction of renewable energy of the European energy market. “Hydro” is the fraction of hydro power an Wasserkraft. Source, Eurostat