Using solar and wind power for clean energy generation relies on the weather, which can be unpredictable. In addition to seasonal changes and daily variability, energy planners must also account for rare and extreme weather events. Traditional planning methods often fail to capture these events accurately, resulting in systems that are either overly expensive or insufficiently reliable.

In this work, the researchers aimed to develop a more effective way of planning energy systems that can cope with real climate variability. They built an artificial intelligence model trained on 30 years of wind and solar data from Pingtan in China. The model generated thousands of realistic weather scenarios, including both typical conditions and extreme events.

The researchers then used these scenarios to determine the most cost-effective and reliable combination of energy technologies. Their Integrated Energy System included solar panels, wind turbines, batteries, hydrogen production and storage, and a connection to the national electricity grid. The optimal system configuration was identified by testing its performance across all of the AI-generated scenarios.

The optimisation showed that the most effective renewable energy system combines several technologies working together. Wind and solar provide the main source of electricity, batteries manage short-term fluctuations in supply and demand, hydrogen storage provides backup during extended periods of low renewable generation, and the grid acts as an additional safety net.

The key finding is that batteries and hydrogen have complementary roles: batteries are best for balancing daily variations, while hydrogen is better suited to storing energy over weeks or months. Together, they help create a renewable energy system that is both reliable and cost-effective.

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Energy policy and public opinion: patterns, trends and future directions by Parrish Bergquist, David M Konisky and John Kotcher (2020)