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System integration of low-carbon electricity

This section systematically investigates the impact of different electricity storage energy capacities and efficiencies on integrating low-carbon electricity generation. It focuses on variable generation from wind and solar power and uses the Great Britain power system as an example. The analysis is based on meteorological wind and solar data, assumes that electricity can flow unconstrained from generation to consumption and that all generation capacity beyond wind and solar is fully flexible.
 

An initial step is to identify the ideal ratio of wind and solar in the generation mix that best matches hourly demand to minimize the need for electricity storage. Hourly wind and solar generation data are scaled to meet a certain share of total electricity demand overall. Hourly electricity demand is then subtracted from the scaled wind and solar generation. This reveals how much of this generation actually meets demand and is consumed, rather than curtailed.


Figure 1 shows that a wind:solar ratio of 85%:15% best coincides with Great Britain’s hourly electricity demand pattern. If wind and solar generate 100% of total electricity demand at this ratio, they actually provide 78% of hourly demand. In contrast, if the wind:solar ratio was 0%:100%, only 41% could be consumed. The analysis also reveals that below 25% wind and solar generation there is only minimal mismatch (< 1%) with hourly demand for any wind:solar ratio. For the ideal wind:solar ratio this value increases to 50%. There would be no need for electricity storage to integrate excess wind and solar generation below these penetration levels.

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Figure 1 – Relationship between the share of wind and solar electricity generation and the share which can be consumed (accounting for mismatch between supply and demand) without electricity storage. Different colours refer to different shares of wind versus solar generation. Based on meteorological and demand data for 1991–2019 for Great Britain. Average annual electricity demand is ~320 TWh.

The next step is to model the ability of storage energy capacity to integrate excess solar and wind generation. Figure 2 shows the results for different sizes of 100% efficient electricity storage. The capacities are given relative to the average hourly electricity demand (average load hours) and in absolute terms (TWh). Assuming 20% wind and solar overcapacity, such that total generation amounts to 120% of total electricity demand, 84% could be consumed without any storage (compared to 78% without overcapacity in Figure 1). Storage systems that could supply average demand for four hours, a day, a week or a month would increase that share to 88%, 94%, 99%, or 100% respectively.

Figure 2 – Relationship between the share of wind and solar electricity generation and the share which can be consumed (accounting for mismatch between supply and demand) with different energy storage capacities, assuming 100% round-trip efficiency. Different colours refer to different amounts of energy storage capacity. Based on meteorological and demand data for 1991–2019 for Great Britain. Average annual electricity demand is ~320 TWh. hours = Average load hour, indicating the average hourly electricity demand.

A zero-carbon electricity system could be realized with ~110% solar and wind penetration and an electricity storage energy capacity of 27 TWh (average demand for one month). Alternatively, ~140% penetration and 6 TWh (one week) would suffice. This shows how wind and solar overcapacity can reduce the need for electricity storage energy capacity.


However, these values are significantly lower than in other studies. The reason is that electricity storage systems are not 100% efficient. Applying a 40% efficiency to the 27 TWh would increase the required energy storage capacity to 67 TWh. Figure 3 shows the impact of integrating wind and solar generation for energy storage capacities of different sizes and different round-trip efficiencies (RT). The three storage types could be classified as:

 

  • Small, short duration: Energy storage capacity of ~0.05% of total annual electricity demand (0.15 TWh) that can supply average demand for 4 hours at 80% round-trip efficiency; Example: lithium-ion battery storage
     

  • Medium size, medium duration: Energy storage capacity of ~0.18% of total annual electricity demand (0.58 TWh) that can supply average demand for 16 hours at 60% round-trip efficiency; Example: compressed air storage (efficiency is average between adiabatic and diabatic type)
     

  • Large, long duration: Energy storage capacity of ~20% of total annual electricity demand (~27 TWh) to supply average demand for 730 hours (1 month) at 40% round-trip efficiency; Example: hydrogen storage.

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Figure 3 – Relationship between the share of wind and solar electricity generation and the share which can be consumed (accounting for mismatch between supply and demand) with different energy storage capacities and round-trip efficiencies. Different colours refer to different sizes and round-trip efficiencies of energy storage capacity. Based on meteorological and demand data for 1991–2019 for Great Britain. Average annual electricity demand is ~320 TWh. hours = Average load hour, indicating the average hourly electricity demand. RT = Round-trip efficiency.

The analysis reveals that up to 80% wind and solar generation, the small, short-duration storage is as effective as medium- and long-duration storage (only 1.5% difference in the amount of wind and solar generation that can be used at the hourly level). Up to 90% penetration, medium-sized and medium-duration storage is as effective as long-duration storage (only 1% difference). However, to fully meet electricity demand with wind and solar generation, large, long-duration storage is needed along with over-building renewables. In this example, it would amount to ~27 TWh at 40% round-trip efficiency and 40% wind and solar overcapacity. Such large-scale electricity storage systems do not yet exist.

 
The table below summarizes the key insights on the role of electricity storage in integrating wind and solar generation for Great Britain’s power system and other major power markets.


The key insights are:

  • There are optimal wind:solar ratios that minimize the mismatch between wind / solar generation and hourly demand: For markets with high wind and low solar resource (like Europe) it is > 80:20. For markets with better solar resource, it changes to ~70:30.

  • No storage is needed if the share of wind and solar generation is < 25%: At this share, wind and solar generation can always be fully integrated with demand (condition: the remaining generation capacity is flexible).

  • No storage is needed if the share of wind and solar generation is < 45% and the optimal wind:solar ratio is applied: At this share and ratio, wind and solar generation can always be fully integrated with demand (condition: the remaining generation capacity is flexible).

  • Small, short-duration storage is sufficient to integrate wind and solar generation if its share is below 80%: The difference in integrating this share with hourly demand compared to medium-sized and -duration storage systems is < 3%.

  • Medium-sized and medium-duration storage is sufficient to integrate wind and solar generation if its share is below 90%: The difference in integrating this share with demand compared to large, long-duration systems is < 3%.

  • Large, long-duration storage systems and wind and solar overcapacity are required to meet 100% of demand with wind and solar generation alone: All markets can achieve full integration of wind and solar generation with a storage system sized at ~20% of total annual electricity demand and ~140% wind and solar generation capacity relative to total annual electricity demand.

  • Wind and solar overcapacity can significantly reduce the required electricity storage energy capacity: A 100% efficient store sized to ~20% of annual electricity demand could ensure demand is fully met with 110% wind and solar generation; at 140% wind and solar generation this would reduce to a size of ~2%.

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Schmidt, O., & Staffell, I. Monetizing Energy Storage - A toolkit to assess future cost and value. Oxford University Press. Forthcoming. 

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