Hourly energy model example 1: Variable Supply

This first example in the hourly energy modelling tool model's the hourly output of a given installed capacity of wind, wave, tidal or solar. The model really isn’t doing much its just loading the capacity factors for every hour from the 10 year dataset and multiplying the capacity factor by the installed capacity.

The total electricity generated is calculated as the sum of the electricity generation in each hour and printed along with the capacity factor at the end.

This example is useful for just seeing what the ZeroCarbonBritain renewable capacity factor dataset looks like, you can zoom and pan through the datasets for onshore wind, offshore wind, tidal, wave and solar pv, click on the link below to open the tool:
Online tool: > navigate to 1. variable supply

The units are really not important the example could just as well be in MW's or GW's. kW's where chosen as the other model examples in the series are focused around building a hourly model that's relatable to an average households energy demand. The kW's of installed capacity could just relate to a small share of a much larger wind turbine, solar farm, wave or tidal power installation.

Running the model for each of these generation types with the same installed capacity the results are as follows:

Onshore wind Offshore wind Wave Tidal Solar
Installed capacity 1.0kW1.0kW1.0kW1.0kW1.0kW
Annual generation 2834 kWh4204 kWh2482 kWh2092 kWh826 kWh
Capacity factor 32%47%28%23%9%

In all of the examples above the installed capacity of the renewable generator was the same (1.0kW) but we can see straight away that there is a significant difference in the total electricity generated by each type. A unit of offshore wind generates just over 5 times as much energy as a unit of solar pv using the zerocarbonbritain dataset. By itself this is not enough information to evaluate the effectiveness of a technology, we would need to compare the costs per unit of installed capacity, embodied energy per unit of installed capacity, how well a particular solution matches demand, the land areas required, the availability of the resource to name just a few of the many factors that need to be weighed up but it does highlight one of the important factors.

Python example source code
Alongside the online javascript modelling tool there are a series of python versions of the examples which are simpler to follow as they dont include all the code to create the visual output, they just print out the main results at the end.

The following 19 lines of python code are all you need to load the ZeroCarbonBritain dataset and run through all 87,648 hours, calculating the power output for each hour and accumulating the total energy supplied over the 10 year model period:

# dataset index:
# 0:onshore wind, 1:offshore wind, 2:wave, 3:tidal, 4:solar, 5:traditional electricity
gen_type = 4

installed_capacity = 1.0 # kW

# Load dataset
with open("../dataset/tenyearsdata.csv") as f:
    content = f.readlines()
hours = len(content)

print "Total hours in dataset: "+str(hours)+" hours"

total_supply = 0

for hour in range(0, hours):
    values = content[hour].split(",")
    supply = float(values[gen_type]) * installed_capacity
    total_supply += supply

capacity_factor = total_supply / (installed_capacity*hours) * 100

print "Installed capacity: %s kW" % installed_capacity
print "Total supply: %d kWh" % total_supply
print "Capacity factor: %0.2f%%" % capacity_factor

Next: Variable supply and flat demand - investigating the degree of supply/demand matching To engage in discussion regarding this post, please post on our Community Forum.