Hourly energy model example 3: Variable supply and traditional electricity demand

The ZeroCarbonBritain dataset includes 10 years of hourly traditional electricity demand data for the UK. The previous example compared renewable supply data with a flat demand profile, this example explores the effect of the variable traditional electricity demand profile with its day time peaks and night time low on supply/demand matching for the different renewable energy generators.

The screenshot below gives a flavour for what the traditional electricity demand profile looks like in blue, the black line is the supply from onshore wind, using the tool you can compare traditional electricity demand to: onshore wind, offshore wind, tidal, wave and solar power.


Online tool: http://openenergymonitor.org/energymodel > navigate to 3. Variable supply, traditional electricity demand and oversupply

These are the results for the amount of demand supplied directly for each generation type, matching annual supply totals with demand totals:

Onshore wind Offshore wind Wave Tidal Solar
Installed capacity 1.17kW0.79kW1.33kW1.58kW3.98kW
Percentage of demand
supplied directly
66.5%76.7%75.2%57.0%42.1%
Percentage of time demand is
more or the same as the supply
40.7%46.5%44.7%38.7%31.1%

Interestingly they only change marginally. Solar PV makes a gain 2% on the demand supplied directly which reflects higher demand in the day vs night time and we see a couple of other 1% changes but the differences are quite marginal and smaller than the difference between each renewable energy type so we don’t really see any change of order.

Increasing the degree of supply/demand matching between a variable renewable supply and traditional electricity demand by over supply

The are multiple ways of increasing the level of supply/demand matching or reducing the unmet demand. Over-supply is one way we can do this and is one of the measures used in the ZeroCarbonBritain scenario. In the previous examples we sized the installed capacity of the renewable electricity generating technologies to produce over the 10 year model period the exact same amount of electricity as was used in the 10 year period.
We can re-run the same model but with installed capacity amounts set to 110%, 120% or 130% of demand

Oversupply: 110%
Onshore wind Offshore wind Wave Tidal Solar
Installed capacity 1.28kW0.86kW1.46kW1.74kW4.39kW
Percentage of demand
supplied directly
68.9%79.6%77.958.9%43.0%
Percentage of time demand is
more or the same as the supply
44.1%51.8%50.0%41.7%32.4%

Oversupply: 120%
Onshore wind Offshore wind Wave Tidal Solar
Installed capacity 1.28kW0.94kW1.60kW1.89kW4.79kW
Percentage of demand
supplied directly
71.1%81.9%80.3%60.4%43.8%
Percentage of time demand is
more or the same as the supply
47.2%56.5%54.3%44.3%33.5%

Oversupply: 130%
Onshore wind Offshore wind Wave Tidal Solar
Installed capacity 1.51kW1.02kW1.73kW2.05kW5.19kW
Percentage of demand
supplied directly
72.9%83.9%82.2%61.7%44.5%
Percentage of time demand is
more or the same as the supply
50.1%60.7%58.1%46.7%34.4%

For every 10% of demand increase in supply we see 1-3% improvements in the percentage of demand supplied directly and 1-5% improvements in the amount of time demand is more or the same as supply.

The python code for the above examples is very similar to the previous example for the flat demand profile and can be downloaded here: http://openenergymonitor.org/energymodel/#python

The next example looks at the question of complementarity between different renewable energy types and asks the question what might the optimum capacity mix point be between wind and solar for a given electricity price point. To engage in discussion regarding this post, please post on our Community Forum.