As the human population and world economy grow, so does the energy demand. Today, the world is grappling with the daunting task of achieving a balance between energy demand and supply. However, the current energy infrastructure is neither sufficient nor sustainable. The use of non-renewable sources like coal and oil is one of the leading causes of climate change and is unsustainable, owing to its perishability. To protect the environment and address the growing energy demands at the same time, we must shift to a more sustainable energy distribution system. And that’s where the smart grids come in.
What are smart grids?
Smart grids are modern electricity network systems, that use digital and other advanced technologies to manage the electricity delivery from producers to consumers. Unlike traditional grids, where power is distributed from centralized sources such as nuclear, coal and gas power plants to consumers, a smart grid is a decentralized network of energy sources (most of them being renewable) that are installed closer to where the electricity is required. This means the electricity is produced locally, reducing the time for transmission of electricity over long distances, consequently, enhancing the effectiveness of the system. It uses sensors and control systems which automatically regulate how much energy is being generated and supplied to various locations. It enables the energy providers to supply energy to areas and times where it is needed the most and helps customers make smart environment-friendly choices.
Integrating Artificial Intelligence in Smart Grids
These smart grids can be further augmented by the incorporation of AI systems. AI can play an important role in making smart grids more efficient. Its data-driven algorithms can analyze the previous data on energy demand and supply, weather patterns and other necessary factors, helping the operators to decide when and where to generate and supply energy and in what amount. This results in more efficient use of resources, making the whole process more cost-effective, and environment friendly.
To incorporate AI in smart grids, both hardware and software systems are required. The former includes components such as smart meters, communication networks and control systems, and the latter includes elements such as predictive analytical platforms.
The hardware is used to collect data on energy consumption patterns, demand and supply, and other metrics. The collected data is then sent to the central control system, where it is analyzed and processed using AI software. Based on this, the system can predict future energy usage patterns and take measures to address them. For example, if it is detected that energy usage is likely to rise in an area, it can automatically adjust the power supply network to make sure there’s enough electricity to meet the growing demand. The software can also identify potential power system failure scenarios and make changes accordingly.
Another usage of AI is that it can optimize the use of renewable energy sources. Renewable energy resources such as solar power and wind energy are unpredictable, which is why it is difficult to incorporate them in areas such as homes and businesses, which require a great amount of reliability and stability. But AI can anticipate when renewable energy sources will be available and modify energy usage patterns accordingly, thereby, reducing the difficulties of reliability and stability. For example, if it is detected that it’s going to be windy or sunny in a certain area, it can automatically adjust the grid to take advantage of that source, reducing its dependence on traditional energy sources and saving energy.
AI can also be used to detect and identify faults in the grid, making maintenance and repair more efficient. Since it uses a two-way communication channel, it helps significantly improve the effectiveness, accuracy, and efficiency of the power systems.
How AI-enabled Smart Grids are revolutionizing Energy Management in developed countries?
Many developed countries today are integrating AI in smart grids to monitor and control their power systems and address challenges such as reducing energy waste and increasing the use of renewable energy sources.
Here are a few examples of developed countries using AI in smart grids:
Denmark is one of the most advanced countries in terms of renewable energy use and has successfully integrated AI into its smart grid. The country has set a target of a 70 per cent reduction in CO2 emissions by 2030 and 100 per cent climate neutrality by 2050 and has implemented several initiatives to make this a reality. One of these initiatives is the use of AI-enabled smart grids to optimize energy usage and improve grid performance.
Denmark has also implemented several smart grid projects across the country. One such example is the Skaerbaek Power Plant. The plant uses a combination of wind, solar and biomass energy sources to generate electricity. The AI algorithms are used here to predict the availability of these energy sources and adjust the plant’s energy production accordingly. This allows the plant to produce energy more efficiently and with less waste.
The US is also using AI to improve the efficiency and reliability of its power grid. For example, the department of energy’s Advanced Research (ARPA-E) projects such as the “grid resilience and intelligence platforms” which uses AI to detect and respond to cyber-attacks on the power grid.
Smart grids in developing countries
While developed countries have already adopted this technology, the developing world is yet to transform their energy systems and adopt AI and other such technologies to provide flexibility and improved performance in their current power generation system. The existing challenges in these are due to a lack of data samples, unreliable infrastructure, and other complications. However, AI-enabled smart grids can transform the decaying power generation system by optimizing the power supply, analyzing power usage, and identifying faults. Developing countries can learn from the experiences of their developed counterparts and accelerate their energy systems to a more advanced level.
One such example is India, which is actively involved in the implementation of smart grid technology in the energy sector. The Indian government has launched several initiatives to promote the development of smart grids such as the National Smart Grid Mission. The motive of these initiatives is to develop policies and regulations that support the integration of smart grid technologies into the Indian power system and promote research and development in this area. Apart from these policies, there have been several pilot projects such as Smart Grid Pilot Project in Puducherry, to demonstrate the benefits of smart grid technologies such as advanced metering infrastructure, outage management systems, and demand response programs.
However, there are still many challenges such as a lack of adequate infrastructure, limited technical expertise and the need for significant investment to overcome in implementing AI-based smart grid technologies in India and other developing countries.
To conclude, smart grids can play a significant role in attaining sustainability. By optimizing energy demand and supply, reducing waste, and incorporating renewable energy sources, smart grids can contribute to reducing carbon emissions and combatting climate change. However, the implementation of smart grids relies heavily on the integration of AI technologies, which may pose challenges for developing countries. Despite these challenges, developing countries can learn from the experiences of developed countries and adopt smart grid technologies and mold them according to their needs, thereby contributing to a more sustainable future for all. As the world shifts towards a more sustainable future, the role of smart grids continues to grow to accomplish this goal.