What are the effects of increased penetration of decentralized renewable energy onto the grid?
With the growing environmental concerns about climate change and the need for decarbonization, many private sector organizations, governments and civil society have committed to a 100% renewable energy future.
As of late 2016, more than 300 cities, municipalities and regions including Frankfurt, Vancouver, Sydney, San Francisco, Copenhagen, Oslo, Scotland, Kasese in Uganda, Indionesia’s Sumba island and the Spanish Island of El Hierro have demonstrated that transitioning to 100% RE is a viable political decision.
It is no doubt such ambitious targets to transition to 100% renewables will require new tools, concepts, and technologies to cope with the increased penetration of intermittent renewable energy into the grid. The good news is that technology developments, in the artificial intelligence and analytics space has already created tools and solutions needed to enable the decarbonization of the economy according to the International Renewable Energy Agency (IRENA).
As such, the International Renewable Energy Agency (IRENA) has developed solutions in its recent report on the “Innovation Landscape for a Renewable Powered Future” which provides a toolbox of solutions for policymakers and guidance on how to apply them system-wide in a coherent and mutually-reinforcing way.
In particular, these solutions center around the application of digital technologies such as Artificial Intelligence (AI), big data and analytics in increasing flexibility in the system for larger integration of renewable energy.
According to IRENA, Artificial Intelligence (AI) and big data, the Internet of Things and batteries are innovative solutions that will enable massive solar and wind use and amplify the transformation of the power sector based on renewables.
Why AI, Big-Data, and Analytics?
The increasing electrical loads such as electric cars, energy storage (batteries or pumped hydro) as well as decentralized renewable energy power such as rooftop solar PV systems, commercial solar, and wind power systems will need a more stable grid or a smart grid.
A smart grid is able to learn and adapt based on the load and amount of variable renewable energy put into the grid as a result of having lots of rooftops solar PV, other extra loads to the grid such as electric cars, energy storage (batteries and pumped hydro) and increasing decentralized intermittent renewable energy.
Without a smart system using artificial intelligence (AI), big data and analytics, grid operators will definitely not cope with the changing electrical loads and the increasing penetration of renewable energy into the grid.
Also, at its core, AI is a series of systems that act intelligently, using complex algorithms to recognize patterns, draw inferences and support decision-making processes through their own cognitive judgment, the way people do.
How can AI support large integration of renewable energy?
Since renewable energy is very intermittent in nature as we would expect because there is no constant wind or solar generation due to weather changes, renewables such as solar and wind can be unreliable and many utility companies utilize energy storage (batteries or pumped hydro) to deal with this issue.
Excess solar or wind power is stored during low demand times and used when energy demand goes high. As a result, AI can improve reliability of solar and wind power by analyzing enormous amounts of meteorological data and using this information to make predictions and knowing when to gather, store and distribute wind or solar power.
On the other hand, AI used in smart grids can be used to balance the grid especially when rooftop solar and other decentralized renewable energy are involved and put into the grid. AI systems utilizing neural networks or complex algorithms to recognize patterns associated with various loads (electric vehicles or energy storage) and increased rooftop solar or other forms of distributed energy (wind or solar) which can make the system to be unstable. The most efficient way to balance this variability in the system is through AI in analyzing grids before and after they absorb smaller units, and in working to reduce congestion.
The IRENA’s report Innovation Landscape for a Renewable Powered Future explains these new AI tools and digital technologies that will support the deployment of renewables as the power sector complexity continue to increase.
According to IRENA, most of the advances currently supported by AI have been in advanced weather and renewable power generation forecasting and in predictive maintenance. However, in the future, AI and big data will further enhance decision-making, planning and supply chain optimization while increasing the overall energy efficiency of the energy systems.
For the renewable energy sector, AI and analytics can support it in several ways such as better monitoring, operation, and maintenance of renewable energy.