Previous post ended with the statement that seasonal storage is needed and this was illustrated in that post with a table of the sum of the deficits by month. This table showed that the sum of the deficits was by far the highest during the summer months, especially in July and August. However, my focus in that post was solely on those periods that had a deficit and, looking closer, this was rather one-sided. Although it is true that the periods with a deficit are the most dominant during the summer months, there are also times in July and August when there is a surplus, albeit rare and far between compared to the rest of the year. Let’s now also include the sum of the surplus in that table and see whether this will affect the conclusion of previous post.

This is the table, completed with surplus and the final balance by month:

Table 1: Balance 1980 →2018 by month

Month	Production	Demand	Periods of deficit (GWh)	Share of total deficit (%)	Periods of surplus (GWh)	Share of total surplus (%)	Balance (GWh)	Share of total balance (%)
January	20,801,447	13,672,074	367,881	9.3	7,497,255	8.1	+7,129,374	8.1
February	19,354,951	11,666,944	243,580	6.1	7,931,586	8.6	+7,688,006	8.7
March	23,312,224	12,113,338	104,759	2.6	11,303,645	12.2	+11,198,886	12.7
April	24,003,049	11,265,088	27,702	0.7	12,765,663	13.8	+12,737,961	14.4
May	22,717,112	12,451,117	99,086	2.5	10,365,081	11.2	+10,265,995	11.6
June	20,199,584	13,866,703	252,155	6.4	6,585,036	7.1	+6,332,881	7.2
July	17,758,369	15,715,708	891,133	22.5	2,933,795	3.2	+2,042,662	2.3
August	16,688,948	15,287,353	1,029,653	26.0	2,431,248	2.6	+1,401,595	1.6
September	18,478,461	13,364,324	371,198	9.4	5,485,336	5.9	+5,114,138	5.8
October	21,014,775	12,126,060	123,086	3.1	9,011,801	9.8	+8,888,715	10.1
November	20,472,701	11,716,382	125,020	3.2	8,881,339	9.6	+8,756,319	9.9
December	19,881,178	13,034,461	328,824	8.3	7,175,542	7.8	+6,846,718	7.7
Total	244,682,799	156,279,552	3,964,077		92,367,327		+88,403,250

There are two opposing trends visible. On the one hand, production of solar and wind power is the highest in March, April, May and October, while at the same time demand is low. On the other hand, production of solar and wind power is the lowest during the summer months July and August, while demand is highest in that same period. This translates into July, August and September having the highest deficit of the year, while also having the lowest surplus of the year. The opposite is true for March, April and May, having the lowest deficit of the year, while having the highest surplus.

Looking at the final balance between deficit and surplus, then every month ends up with a surplus (the lowest surplus is in July/ August and the highest in March/April/May/October). In absolute numbers, the surplus is mostly two or more times the deficit, even during summer. Doesn’t this then mean that even in the summer months storage gets replenished? If the final balance is a surplus in every month, is seasonal storage then really necessary?

Well, no, the deficit doesn’t get replenished during those summer months and yes, as we see further in this post, seasonal storage is still necessary. Remember, the storage method of choice in the Calculator is Power-to-Methane-to-Power. This means that surplus power is converted into methane and it is this methane that is burnt in gas-fired power plants to meet demand when production of solar and wind electricity is inadequate. However, there are quite some losses involved in these conversions. This is the diagram that I created in the post on Power-to-Methane-to-Power:

The efficiency of those processes is pretty low and therefore most of that meager surplus in the summer months will be lost as conversion losses and storage losses. There is simply not enough surplus to replenish storage during the summer months, so it will be necessary to store the surplus of previous months to bridge the shortages during the summer months.

Until this point, I am however still working with averages. The numbers in that table are the averages for each month over the 39 years of the dataset. Systems are not dimensioned by using averages, so let’s get into more detail. How much storage would be needed using the data from the Calculator? And how would the different elements (like demand, production, curtailment) in such a system interact? I already had the demand and production data in Pandas from a previous post, so I wondered whether I could make a simulation of the grid with the dimensions of the calculator in order to answer these questions. Then I could run different scenarios with the goal of finding the smallest storage capacity that doesn’t give shortages over the 39 years of the dataset.

These are the rules that I set for this simulation:

• The underlying data are from the Calculator (demand and capacity/production of solar and wind) and the efficiencies from figure 1.
• What to do when there is a surplus:
  • If that surplus fits storage, then the system will convert the surplus power into methane and puts that in storage (the power put into storage is the surplus minus the conversion losses).
  • If that surplus doesn’t fit storage, then the system will convert as much surplus power (minus conversion losses) into methane as to fill storage completely and curtail the rest.
• What to do when there is a deficit:
  • With enough in storage to match demand, then the system will take methane from storage and will burn it to convert it back to electricity, taking the losses into account (the power taken from storage is the deficit plus the conversion losses).
  • There is also the theoretical case in which not enough power can be draw from storage to meet demand, but this will not apply here because I want to find the smallest possible storage capacity without any shortages, so by definition there needs to be enough in storage to meet demand at all times (otherwise I need to try another scenario with a larger capacity until no shortages occur in the system).
• The gas-turbine efficiency in figure 1 is 30%, but this efficiency depends on the load of the backup installation, so instead of taking a fixed number, I calculated the efficiency based on the deficit that needs to be bridged. I took the 44.88% that was used in the calculator (full load) as the upper border and tweaked the lower border until I got to an average efficiency of 30% in the entire dataset.
• Also implemented are storage losses (that were completely absent in the Calculator). A small amount of storage losses is subtracted from storage at every row and in total these losses amount to 3% of everything that is put into storage.
• Storage is entirely filled at the start (to prevent deficits at the beginning of the graph when hardly any surplus was placed into storage.
• The script runs sequentially over the data and checks at every time interval whether supply and demand are in balance. The goal is to find the smallest possible storage capacity that prevents shortages along the way.
• Not included are things like grid reserve capacity, grid losses,…

The smallest storage that doesn’t cause shortages is slightly below 220,700 GWh, with a discharge capacity of 402.13 GW and a charge capacity of 526.31 GW (instead of the 318.577 GW that Nitsche wrongly assumed to be sufficient based on just the first 365 days of the dataset).

This is how storage state looks like in the optimal scenario over the 39 years:

Clearly visible are the deep downward curves representing the power that is drawn from storage during summer and although it came pretty close on September 7, 2018 at 14:00 (the lowest point of the graph), there were no shortages in those 39 years.

Let’s now look at the relationship between demand, production and curtailment (only for 2018 which is the year when the storage was almost emptied, the full 39 years would make quite a dense graph and it would not be clear what happens):

The blue line is the demand, which is clearly highest during the summer months. The red line is the curtailed power, meaning the power that could not be directly used and also couldn’t be put into storage because it was full, therefore power that was not produced although the sun was shining and/or the wind was blowing. The orange line is the electricity production that is directly used to fill in demand or is put into storage for later (= production by solar and wind minus the curtailed electricity).

Notice when the curtailment is happening: only between the beginning of February until the beginning of July. This because there was enough production to meet demand and the surplus couldn’t go into storage because this was already full. The found capacity is enough to avoid shortages in the most critical year, but might be overkill in other years. Just look at 1999:

This is the result of the worst combination of demand and production in the 39 years dataset (the second lowest demand and the second highest production by solar and wind coincided in 1999, so there might be years with a worse combination of production and demand). I understand that overbuilding and hence curtailment is the central strategy in the 100% renewable electricity calculator, but I don’t want to be a solar/wind installation owner or investor in a system with systemic curtailing.

A capacity of 220,700 GWh, that is enough storage for on average 20 days. That is a lot for a system that supposedly wouldn’t need any backup capacity. But then, how is it possible that someone could come to the conclusion that no significant back-up is needed in a system in which the months with the lowest production have the highest demand and vice versa?

I think that might not be hard to understand. Nitsche’s strategy is to overbuild solar and wind capacity in order to have as much electricity directly produced by solar and wind installations and as little as possible to be filled in by storage. In his spreadsheet with 39 year of data, that amounts to 97.5% of electricity directly generated by solar and wind, therefore only 2.5% of demand needs to be met by backup power.

If one is infected by the averagitis virus, then one might see it this way (also only the year 2018 from the 1980-2018 dataset):

Hey, that should be pretty simple! That 2.5% can on average be met by a backup plant with a capacity of 12 GW… So, what the heck is the big deal? Why is this trustyetverify guy so hellbent on having (lots of) storage in the system, when a modest backup capacity could easily fill in that gap?

Firstly, that 2.5% is ON AVERAGE over 39 years and it is not equally divided over the 39 years. The annual average backup need ranges between 1.52 (1999) and 3.73% (2018).

Secondly and much more importantly, this 2.5% is also not equally distributed over the year, it is concentrated in the summer months when production is at its lowest and demand at it highest, this while the largest surplus is in the beginning of the year. This is the detail of the year 2018 from figure 2:

The steep drop starts on July 9 at 10:00 and reaches its deepest point on September 7 at 14:00. It will take until the beginning of the next year to be filled again, after which the storage just needs to be maintained until the summer months. Because production is highest in the beginning of the year and because at this time the storage only needs to be maintained, there is a large chunk of production that needs to be curtailed. So, there is not only a mismatch with periods of production/demand, but also the period that has the highest production coincides with the period that storage is already full and only needs to be maintained.

July and August already account for more than half of the needed storage capacity of 2018. This means that all the power that will be needed to fill in the shortages during the summer needs to be available in storage before the summer starts and if the goal is a 100% renewable grid with solar/wind/storage, then the only way is seasonal storage.

Looking at the storage requirement from the average values it all seems so very simple. The average storage need is so incredibly low that it might be tempting to assume that no storage whatsoever is needed, as long as enough electricity is produced and stored on average. But when one traverse sequentially over the dataset with respect for the requirement that demand should be met by production and storage at all times, then this tells an entirely different story. A story that is completely hidden by the averaging strategy used in the 100% Renewable Electricity Calculator. Averages don’t apply here because the surpluses and deficits are not equally spread over the year and production needs to meet demand AT ALL TIMES, not just on average per year or on average per 39 years.

via Trust, yet verify

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December 30, 2023 at 03:55PM