Balancing power supply and consumption has become more complex with increased renewable energy generation. Because it depends on variable weather conditions, energy from wind turbines and solar panels is intermittent and unpredictable compared to traditional oil- or coal-fired plants. But timely and smart use of data can make management of power grids supplied from renewable sources more efficient. This can improve the penetration of renewables and reduce carbon emissions, says TESTBED project coordinator Hongjian Sun, a Reader at the Department of Engineering, University of Durham, United Kingdom. “The key lesson we learned from the TESTBED project is that data itself is very useful for integrating all power generation and demand, particularly with renewable energy sources,” he says. “TESTBED is about collecting data and using it to forecast supply and demand, and seeing where we have a bottleneck to resolve,” says Sun. The project, supported by the EU’s Marie Skłodowska-Curie programme, developed optimisation programmes to model improvements in energy efficiency and carbon emissions. “The simulation for the city of Durham in North East England found that by integrating data and transmitting data in a timely way, carbon emissions are reduced by up to 76 % using renewable sources and a smart grid system,” Sun says.
Importance of data
“If we keep the same energy cost and use the data to improve the system’s operation, the penetration of renewable energy can be increased from 20 % to 70 % in a distribution system – it shows the huge potential of using data,” he adds. Smart grid management uses data from a multitude of sensors measuring weather conditions, electricity generation and power transmission to substations and households. Smart meters monitor consumption by households, including measuring individual appliance usage. “We use social media, for example Twitter data, using keyword searches to see if there is a big event or a football game somewhere. This is likely to increase power demand,” Sun says. “Transmitting data is also important as we require frequent, real-time updating to spot and clear grid bottlenecks, improving reliability.”
Sensor and meter data is downloaded to a computer which runs software including artificial intelligence (AI) algorithms to improve optimisation of supply and consumption. “Using AI, we explore weather data and data from several regions, not just from yesterday but from the past year, and we use AI machine learning to train the system to have better prediction capability,” Sun notes. “The work on the TESTBED project showed that the system worked.” The system was tested in laboratories at the Chinese Academy of Sciences, the Greek telecommunications organisation OTE-Greece and the UK’s Heriot-Watt University. Simulations were run at Durham University’s Smart Grid Laboratory which hosts a Real Time Digital Simulator (RTDS) system. An RTDS is used for modelling a low-voltage power network which is connected to emulators of photovoltaic cells, wind turbines, electrical energy storage as well as a range of low-carbon technologies. The simulation assesses network constraints while the laboratory monitors electricity consumption by domestic appliances. The second stage of the project, TESTBED2, has received EU funding to develop a scalable TESTBED system that can be applied to larger and more complex smart grids supplying more consumers.
TESTBED, TESTBED2, energy, renewable energy, renewables, wind turbines, solar panels, power grids, data analysis, artificial intelligence, fossil fuels, carbon emissions, smart grid, simulation