IoT Sensors and Actuators: Transforming Legacy Grid Infrastructure

The Internet of Things (IoT) is digitizing and automating numerous industries. It is the key technology behind smart cities, which aim to create a cleaner and better-managed urban environment than cities today. IoT technologies are also the cornerstone of the transition to renewable energy and smart grids.

IoT sensors are now implemented within power grids to upgrade the decades-old legacy infrastructure. These sensors monitor different grid aspects to provide better control of operations and renewable energy management.

Power grid infrastructure using IoT. Image used courtesy of Adobe Stock

The Smart Grid Transition

Many grids worldwide currently rely on legacy infrastructure, including substations, transformers, and transmission lines built back as far as the 1950s. These assets can be digitized at the physical level by using IoT sensors. IoT sensors and actuators are placed at all points within the physical network to monitor the voltage levels and current flow in real time. These sensors assist operators in better understanding grid performance and allow them to plan accordingly.

Sensors and actuators also form the building blocks of metering infrastructure and the energy distribution network. When combined with the smart algorithms used in IoT-enabled networks, they play a major role in detecting grid faults and alerting operators to any potential anomalies in the data patterns. Operators can use this information to prevent blackouts and downtime.

IoT-enabled grids are also more adept at managing grid fluctuations and facilitating more efficient supply and demand scenarios. They can encourage people to use less energy or control the energy supply from renewable microgrids and battery energy storage systems. Overall, smart grids are making the grid more resilient and robust to fluctuating demand. This is more important than ever since intermittent renewables are attached to the grid, and electric vehicles cause localized energy spikes and intermittent high loads.

How IoT Supports the Energy Transition

The power grid’s next generation will rely significantly on intermittent renewable energy. These depend on weather conditions and can’t be switched on and off like traditional energy production methods. The loads generated can vary, with down periods and spikes with large amounts of energy produced.

Smart grid with renewable energy integration. Image used courtesy of Pandiyan et al.

IoT sensors and actuators can adjust the energy capture rates of solar and wind systems in the grid, depending on the supply and demand requirements. In high production periods, some energy can be lost, so pairing the energy-capturing control algorithms with battery and other energy storage systems allows it to be stored for later use when it is needed more. The energy can then be supplied when demand rises or grid disruptions occur. Without IoT technologies’ advanced controls and data analysis capabilities, optimizing energy production and storage would be less robust and efficient.

While the physical infrastructure is important, the real “smart” part of the smart grid comes from the advanced control algorithms and two-way communication networks implemented alongside it. These software systems communicate all the data in real time to operators and allow them to make decisions when issues and load situations arise.

The Rise of AI in Smart Grids

While smart grids are highly functional and can provide efficient data management and analysis operations, artificial intelligence and machine learning algorithms are used increasingly within them. AI and ML provide much more advanced data pattern recognition capabilities to spot anomalies and protect the grid from potential cyber threats. More smart connected devices and nodes in the network, such as sensors and communications technologies, introduce more potential entry points for hackers.

Machine learning data analysis for weather and outage patterns. Image used courtesy of Argonne National Laboratory

Smart grids generate a massive amount of data across many locations, so machine learning is helping to make better use of this data and provide more advanced real-time updates. Additionally, because renewable energy generation is intermittent, spotting patterns and trends for energy distribution purposes can be difficult. Using AI gives the grid a much better chance of predicting the energy generation capabilities of different distributed energy resources in the grid based on the local weather patterns and historical data to better optimize both renewables and storage systems and supply energy to consumers for longer periods without downtime.

IoT Makes it Easier to Integrate Renewables in the Grid

While IoT-enabled smart grids can better manage renewables, they also make it easier to integrate renewables into the grid. The data transmission to a central server from new renewable energy sources allows their energy loads to be better managed and optimized. Predicting the demand, balancing the load, and reducing the peak load of new renewables in the grid makes integration much easier and smoother.