Electrical energy storage to make wind and solar PV resources disptatchable

by Jarrad Wright MSc PrEng, Energy Exemplar (Africa)

The transitioning to a lower carbon emissions path in most regions of the world has driven significant

investments in alternative renewable sources of electrical energy with particular focus on solar photovoltaic (PV)

and wind technologies. The typical argument against these technologies are issues related to their variability and

unpredictability and management thereof. This paper attempts to show the value that electrical energy storage

could add to a power system that includes a share of solar PV and wind. More specifically, the ability of energy

storage to make these intermittent resources dispatchable.

1. Introduction

It is well known that the energy policies of many regions around the world are focusing on carbon emission reductions.

As a result, there has been a significant shift away from fossil fuel dependence and focus on increased levels of

renewable sources of electrical energy dominated by solar photovoltaic (PV) and wind technologies (especially where a

lack of the majority renewables resource (hydro) is available). The typical arguments against solar PV and wind

technologies are predominantly related to issues of cost and integration into the overall power system. The

differentiation from classical generation technologies (coal, gas, hydro and nuclear fired technologies) is as a result of

their future power output not being controllable and thus deemed to be non-dispatchable resources.

Although still in a nascent stage of development (with the exception of pumped storage hydro applications), electrical

energy storage can provide value in a number of applications considering the increased penetration of solar PV and

wind power at a project as well as system operator level. Applications could include inter alia power plant

dispatchability, reductions in solar PV and wind curtailment, ancillary services provision (reserves), transmission loss

reduction, transmission investment deferral and congestion management [1].

Via a generic analytical model developed in PLEXOS® [2], this paper attempts to show the technical value that storage

can provide considering envisioned high levels of solar PV and wind penetration into the power systems of the future.

It does not attempt to assess in detail the various energy storage technologies nor the economical/financial feasibility of

energy storage. Instead, it focuses solely on providing a comprehendible view of the technical value that electrical

energy storage could provide to a power system as it relates to the variable and unpredictable nature of these resources.

The paper is structured to provide an overview of electrical energy storage as well as solar PV and wind followed by the

presentation of an application of electrical energy storage as it relates to the dispatchability of wind and solar PV.

2. Brief electrical energy storage overview

Electrical energy storage manifests in a number of technologies. The main technologies are electro-chemical, electro-

mechanical, thermal, pumped storage and hydrogen. There are 1 242 energy storage projects around the world totaling

130.3 GW of capacity as of the date of writing. The evolution of energy storage by technology is shown in Fig. 1 [3].

The number of energy storage projects and installed capacity by technology worldwide are summarized in Fig. 2

and Fig. 3 [3]. As can be seen, the dominant energy storage technology by power capacity is pumped hydro storage

(96%) with other storage technologies like thermal and electromechanical following this. It is interesting to note the

exponential growth of newer energy storage technologies (thermal and electrochemical) in recent years.

Although a range of studies have been performed on the future market for electrical energy storage worldwide, there is

no consensus as yet on the potential for electrical energy storage. The IEA predicts an additional 310 GW of storage

capacity by 2050 in the major regions of China, India, European Union and the United States based on their 2OC

scenario [4]. Studies performed by Boston Consulting Group in 2010 [5] and 2011 [6] have defined a market potential

for energy storage of 330 GW by 2030 already. It is clear that there is a significant market for electrical energy storage

into the future.

Examples of each electrical energy storage type are shown in Fig. 4 to Fig. 8. The typical capacity and energy storage

capabilities of each type of storage is shown in Fig. 9 along with how each technology could then be applied [7] such as

bulk energy (energy supply capacity and energy time shifting (arbitrage)), transmission/distribution grid support

(ancillary services, congestion management, upgrade deferral) and power quality improvement [7].

Fig. 1: Energy storage capacity evolution by technology (MW) [3]

Fig. 2: Share of energy storage by number of projects [3]

Fig. 3: Share of energy storage by installed capacity (MW) [3]

Fig. 4: 1 932 MW / 15 456 MWh Okutataragi Pumped Storage Power Station (Asago, Japan) [3]

Fig. 5: 32 MW / 8 MWh AES Laurel Mountain Lithium Ion battery storage (West Virginia, USA) [3]

Fig. 6: 100 MW / 250 MWh Kaxu Solar One CSP power plant with thermal storage (Pofadder, South Africa) [3]

Fig. 7: 20 MW / 5 MWh Beacon Power flywheel frequency regulation Plant (Pennsylvania, USA) [3]

Fig. 8: 0.32 kW / 7.68 kWh Thüga- Strom zu Gas Hydrogen Demonstration Project (Frankfurt am Main, Germany) [3]

Fig. 9: Applications of energy storage technologies considering typical capacity and discharge time (energy) [7]

3. Brief wind and solar PV overview

3.1. Wind

Worldwide installed capacity of wind power capacity is shown graphically in Fig. 10 [8]. There was 318.5 GW of wind

capacity installed worldwide by the end of 2013 and in dominant regions including Europe, North America and Asia

(predominantly China), there has been a near exponential growth in installed capacity since 2000 with an annual

average growth rate of 24% for the past decade. The IEA predicts an installed wind capacity of 1 600 GW by 2030

(>4x 2013 levels) and 2 700 GW by 2050 (>7x 2013 levels) based on their high renewables scenario [9] with significant

growth expected from Asia (mostly from China), Europe and USA.

The intermittent nature of the wind resource results in the requirement for sufficient predictions of the expected wind

resource to allow for supply and demand to be in balance as expected by system operators. This has driven large

investments in forecasting technologies in a number of regions including Spain where day-ahead forecasting errors are

one-third of the error experienced in 2008 [9].

Fig. 10: Worldwide installed wind power capacity [8].

3.2. Solar PV

Worldwide installed capacity of solar PV capacity is shown in Fig. 11 [10]. At the end of 2013, 138.8 GW of solar PV

capacity was installed worldwide which is dominated by Europe (mostly Germany), Asia Pacific (including China) and

the Americas. Installed solar PV capacity worldwide has grown even faster than installed wind capacity with more than

5x as much installed solar PV capacity at the end of 2013 compared to 2003. The IEA predicts solar PV capacity to

grow even faster than wind capacity into the future with predictions of 1 700 GW by 2030 (>12x 2013 levels) and

4 670 GW by 2050 (>33x 2013 levels) based on their high-renewables scenario with most of this growth expected to

come from China (and other Asian countries), USA, India and the Middle East with Africa growing from a small base

in 2013 [11].

Over smaller geographical areas, solar PV generation is indeed quite uncertain as a result of cloud cover uncertainty and

atmospheric changes but at larger geographical scales expected solar PV generation becomes better known and thus is

only variable and not necessarily intermittent anymore. This is important to note for countries considering deploying

large amounts of solar PV in specific geographic areas as opposed to spreading the deployment over a larger geographic

area. However, even with intermittency minimized, solar PV generation is still variable and needs to be managed

accordingly.

Fig. 11: Worldwide installed solar PV power capacity [10].

4. Solar PV and wind dispatchability with storage

Based on the aforementioned historical developments of wind and solar PV as well as planned levels of these resources

worldwide, it will become increasingly more challenging and important to ensure that these dominant renewable

sources are integrated in the most efficient manner possible. This will include ensuring that curtailment is minimized,

sufficient alternative capacity is available to provide security of supply, ancillary services are provided in the

appropriate timeframes, sufficient coordinated transmission evacuation capacity is built and market integration is

ensured.

Following on from the above and aforementioned expected applications of electrical energy storage, the following

would likely be the dominant applications of electrical energy storage in the future:

Renewables dispatchability improvement

Renewables curtailment minimization

Ancillary services provision in various timeframes (reserves)

Transmission congestion management and investment deferral

Transmission loss reduction

It is likely that the value proposition of an energy storage project will result from multiple applications (in line with the

technical capabilities of the storage technology opted for). For brevity, this paper only presents the value that electrical

energy storage brings as it relates to the improvement of renewable energy dispatchability (in particular wind and solar

PV). However, this does not attempt to choose an appropriate technology or parameters thereof but instead attempts to

show the technical value that energy storage can provide.

A generic model has been developed in PLEXOS® with the technical properties of the supply side given in Tab. 1. The

penetration by installed capacity of renewables (solar PV and wind) is ~16% with the expected hourly profiles known.

However, the standard deviation of the solar PV and wind expected values are parameterised (2.5%-20% for solar PV

and 5-25% for wind). An example of this is shown in Fig. 13 where a standard deviation of 7.5% for solar PV and 10%

for wind are represented. The demand side is modelled with a generic daily demand profile as shown in Fig. 12.

Storage is modelled at the solar PV and wind power plants with the purpose to ensure that the day ahead expected solar

PV and wind output is maintained at the expected value while the storage modulates power output to ensure this.

The unit commitment and energy dispatch problem is solved on a day-ahead basis using Mixed Integer

Programming (MIP) with/without storage and considering the intermittency of the solar PV and wind power plants. A

Monte Carlo approach is taken to account for this intermittency, with a number of simulation runs performed to assess

the operation of the required storage capacity and energy (which are free to be optimised) based on the standard

deviation of the expected solar PV and wind power plant outputs.

Examples of the difference in unit commitment and energy dispatch for the overall system as well as the OCGT power

plant considering variations in solar PV and wind output are shown in Fig. 14 and Fig. 15 respectively (when no storage

is included). It can be appreciated that the management of intermittent resources such as solar PV and wind creates a

significant challenge for system operators as there are a number of solutions to the unit commitment and energy

dispatch problem depending on the deviations from expected wind and solar PV plant generation.

With storage included, as can be seen in Fig. 16, the unit commitment and energy dispatch is identical across all

samples. The storages included at the solar PV and wind plants generate power into or demand power from the system

to correct for any deviations from the expected day ahead power output from these plants. It is clear that the solar PV

and wind power plants have been made dispatchable as a result of the inclusion of storage at these power plants.

For brevity, the wind power plant storage operation for the day under study is shown in Fig. 17. As can be seen, the

supply of power into the power system (generation) and demand of power from the system (load) varies throughout the

day and is different for each sample that is run. After using the typical Monte-Carlo approach, a reasonable estimate of

the size of the required storage capacity and energy is found (within a defined confidence level) as shown in Fig. 18

and Fig. 19 for selected standard deviation assumptions of the day-ahead expected solar PV and wind power plant

outputs. If one were to assume a day ahead forecast error for solar PV and wind output of 10% and 15% respectively

with a 95% confidence level, the storage required at the solar PV plant and wind plant should be 9% and 13% with an

energy storage requirement of 5.1 h and 4.2 h respectively. For the specific 100 MW solar PV and 200 MW wind plant

modelled, this would mean solar PV and wind energy storage systems of 9 MW / 46 MWh and 26 MW / 109 MWh

respectively.

Tab. 1: Technical characteristics of supply

Name Units

Max

Capacity

Installed

Capacity

Minimum

Stable Level

Fuel

price Heat rate

Ramp

rates

Minimum

Up Time

Minimum

Down Time

MW MW MW USD/GJ GJ/MWh %/min Hrs Hrs

Wind 1 200 200 - - - - - -

Coal 1 2 250 500 125 3 11 2.00% 12 16

Solar PV 1 100 100 - - - - - -

OCGT 5 100 500 25 6 12 20.00% 2 2

Coal 2 3 200 600 100 3 9 2.00% 12 16

Fig. 12: Daily demand profile assumed

Fig. 13: Examples of intermittent solar PV and wind daily profiles (10 samples shown)

Fig. 14: Differences in system energy dispatch as a result of intermittent solar PV and wind generation (2 samples)

Fig. 15: Changing unit commitment and energy dispatch of OCGT plant as a result of wind and solar PV intermittency

(10 samples shown)

Fig. 16: Identical unit commitment and energy dispatch of OCGT plant as a result of storage at wind and solar PV

plants (10 samples shown)

Fig. 17: Wind power plant storage operation (10 samples shown)

Fig. 18: Storage capacity sizing requirement vs confidence level

Fig. 19: Hours of storage vs confidence level (standard deviation (σ) of solar PV and wind output is parameterised)

5. Conclusions

Considering that energy policies of many regions around the world are focusing on carbon emission reductions there

has been a significant shift in focus on renewable energy sources. The typical arguments against these technologies of

cost and integration are as a result of their future power output not being controllable and thus they are deemed to be

non-dispatchable resources. Electrical energy storage can provide value in this regard in a number of applications

considering the increased penetration of solar PV and wind. The specific application presented in this paper is making

these resources dispatchable. An overview of solar PV and wind energy has been given including their significant

growth in installed capacity in recent years as well as expected growth into the future. Following this, via a generic

analytical model developed in PLEXOS®, the issue of solar PV and wind dispatchability and the effect on unit

commitment and energy dispatch was presented. It was shown via the use of a generic model that using a Monte Carlo

approach, the energy storage system capacity as well as energy requirement can be derived as a function of confidence

level based on a parameterised error of solar PV and wind power plant output from expected day-ahead forecasts.

Contact details

For further information, contact Jarrad Wright, Energy Exemplar (Africa), jarrad.wright@energyexemplar.com,

+27(0)79 527 6002.

References

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Energies a Reality,” 2010.

[6] Boston Consulting Group (BCG), “Revisiting Energy Storage: There Is a Business Case,” 2011.

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