What kind of power system will we need 50 years from now? Professor Richard Green explains why an accurate answer to this question is critical to shaping policy

As countries move towards net-zero emissions, policymakers are making decisions regarding the future of energy networks. Both government and business need fast, accurate predictions to help them understand and prepare for the next 30, 40 or even 50 years.

Alongside my colleague Dr Iain Staffell, I have been working to create economic and energy models that can quickly provide accurate information to help inform these decisions. And by making this information as accessible as possible, the benefits can be widely shared.

Long-term outlooks

Knowing the financial viability of a new electricity plant is crucial to deciding whether to build it. However, estimating this accurately requires a long-term perspective. If you want to know if a power plant is going to make money in 2060, you need to know what people are going to have built by 2050, and that depends on what they expect to happen in 2080.

Also, models have to be practical enough to quickly test and compare multiple scenarios, which is no small feat given the complexity of the situation. Our work was designed to strike a balance between speed and accuracy, while still capturing the intricacy of the modern electricity market.

This modelling played a significant role in the European Commission’s decision to give State Aid Clearance for a 35-year, £70 billion contract with the Hinkley Point C nuclear power plant. Our modelling indicated that without government assistance new nuclear power stations would not be profitable until the 2050s, but with financial support, investment is attractive during the 2020s.

This was in 2013, and based in part on our research Hinkley Point C is today being built. It is expected to create up to 25,000 jobs during its construction and save around 600 million tonnes of CO2 emissions over its lifetime.

Predicting the weather

There’s an extra challenge with wind and solar power generation. Unlike traditional power stations they can’t choose when they’ll generate, and we have to know how often the weather will be favourable if we want to predict their behaviour – which is very important now we have so many of them.

To allow others to benefit from the work we did on British wind farms, Dr Staffell, with PhD student Stefan Pfenninger, built the Renewables Ninja. Based on historic weather data from NASA, the system estimates the hour-by-hour generation of a solar or wind system at any location the users chooses. It’s freely available to anyone online, and is an incredibly useful resource for researchers in companies, business, government and academia. For a project developer, it won’t replace doing a test at a particular site, but it shows how a group of PV panels or wind turbines will affect the power system anywhere in the world.

Public perceptions

As energy decisions get made, it has become clear there are a lot of misconceptions among the public about grid technology. Much like the Renewables Ninja, making information as freely available as possible can do a lot to improve public debate.

Using open-source data from the UK’s electricity system, Iain and I, with Imperial colleagues including Professor Tim Green, Co-director of the Energy Futures Lab, set up electricinsights.co.uk with UK energy generator Drax. The website provides free access to almost real-time data about the UK’s energy mix and price, with historical data available all the way back to 2009. The website, and our quarterly newsletter, are helping demystify the debate around decarbonisation, particularly in the media.

This has all been possible thanks to the availability of open-source data from the likes of NASA and National Grid. Very often people have data, but don’t have the knowledge or time to do many of the interesting things that it could be used for. If you make data available other people can do those things, and the sum of human knowledge increases. It’s far better than the alternative of suffering through the same work twice.