ZCBlog: Energy Modelling


Home » ZCBlog: Energy Modelling

Philip James explains the process of modelling a scenario such as Zero Carbon Britain; What are the benefits and the potential pitfalls?

There are three big topics the ZCB energy team are grappling with. These are:

1. Predicting the output of renewables, particularly offshore wind
2. What future energy demand could look like
3. How can energy storage and demand management help us match supply and demand

We hope to find the answers by modelling. And after several jaunts onto the catwalk failed to shed light, we decided to use computer modelling! This is the construction of “a computer program that attempts to simulate a real-life system”. In this case Britain’s future energy system.

To model is to simplify. We simplify time, space and complexity. The team have collected parameters such as wind speeds, solar radiation, electricity demand and temperature for every hour of the last 10 years. We use this as a basis for simulating how future energy systems would have performed under the real-life conditions we have observed in the recent past.

For example, let’s look at how we model offshore wind farms. We want to know how much energy they could supply in the future. We started by identifying around 50 regions which could be suitable for future offshore wind farms and then obtained wind speed data for each of these regions for every hour of the last 10 years through the US Space Agency NASA. Making assumptions about how many wind turbines will be installed in each of these regions now allows us to simulate future offshore wind electricity production patterns, including hourly variations.

For a time scale, on ZCB, we use an hour-by-hour level of detail. To find a value for energy demand we take Britain as a whole and use aggregate demand at that level. However, to determine heating demand we are using average temperature data from the National Grid that is weighted by population. This ensures that the temperature in more populated areas is more prominent in determining the demand.

In terms of complexity, we make many simplifications; from assumptions about how electricity demand varies, to the assumption of a “copper-plate Britain”. This means we assume that there are no restrictions on moving electricity around the country.

The question of simplification in modelling is an interesting one. It is easy to think that increased spatial and temporal resolution or complexity in modelling a system will give more accurate predictions. Therefore, the thing to be done is to launch into modelling to the highest level of complexity time will allow. However, since a model may stand or fall by the accuracy of its assumptions, then building in ever more parameters or increasing the spatial or temporal resolution does not necessarily improve our understanding of a system.

We may in fact lose sight of the fundamental importance of an assumption that was introduced very early on. We have seen this problem in the modelling of the climate system, where models are of ever greater complexity but concerns persist about their ability to predict how climate change will play out in the real world.

It can even be proposed that the ubiquitous ability to build ever more complex models is taking us dangerously away from the scientific method of asking questions, formulating hypotheses, and carefully devising experiments – be they real world or computational – in order to test the validity of those hypotheses. However, alarm bells will ring for many. This is a reductive view of how science must always proceed. Systems cannot always be investigated by reducing them to the sum of their parts. Building and observing computer models can in fact give us answers to questions we had not even fully formulated.

Two fruitful uses of modelling: Lovelock’s Daisyworld and Lorenz discovering the emergence of chaotic behaviour in his attempts to reductively model weather. They teach us about two sides to modelling. Lovelock asked a specific question:

“Can system level regulation emerge from the interaction of “selfish” entities?”

He devised a beautifully simple model to show that it could. Lorenz did not set out to discover chaotic behaviour but was sufficiently alive to the results his model produced that he did, even when the model was not conforming to his preconceived notions of the results he wanted.

From such lofty thoughts the ZCB energy team returns to its spreadsheet columns and rows: carefully devising questions, alive to unexpected results… but mainly just wondering how in the heck you model demand side management?!

Philip James is the Energy Systems Researcher for Zero Carbon Britain.

philip.james@cat.org.uk