Since the electricity transmission grid doesn’t store energy, the amount of electricity fed in must match what is fed out. Today, sensors and smart meters track voltage, current, frequency and power flow, ML algorithms track anomalies, analytics provide comparisons and AI engines provide predictions, enabling quick corrections, preempting grid failures. RE
Focus on energy not derived from fossil fuels is sharp because renewables are diverse, more abundantly available and don’t produce polluting emissions. While past information is handy, environmental forecasts from predictive analytics are vital, as wind patterns and solar intensity change from time to time. AI facilitates optimised turbine placement, angle of solar panels, and precision in storage and discharge routines. Blockchain helps with identification of the energy source.
Storage
Lately, battery technologies have seen remarkable transformations. Solid-state, as well as silicon anode, lithium-ion, lithium-sulphur and other combinations optimise conversion of chemical energy to electrical energy. AI and ML help in figuring optimal configurations and in simulating density, output and cost factors. Hybrid storage, where batteries combine with pumped hydro or thermal storage, and life cycle-related repurpose decisions are other examples of AI interventions. Cybersecurity vulnerabilities need to be secured too.
Smart consumption
Digital solutions and automation seek to intelligently limit usage and integrate RE, while improving both quality and efficiency. Building management solutions, with inputs from sensors and smart meters, optimise HVAC (heating, ventilation and airconditioning), lighting, security and onsite/offsite RE. In industry, energy management systems achieve this, with emphasis on throughput, cutting waste and effective reuse. Digital interventions also prevent equipment breakdowns.
Microgrids
With increasing demand for energy resilience from hospitals, data centres and campuses, deployment of small-scale power grids can integrate with the larger grid. Solar, PV and wind turbines are fundamental here, as are power electronics and microgrid controllers, to integrate inputs from various sources and optimise energy flows. IoT enables real-time tracking, while AI- ML is essential for energy management, including hybrid systems like diesel, solar and biomass, for detecting outages and correcting them.
Digital twins – creating virtual representations of microgrids – helps in modelling and simulations. AI also choreographs islanding of microgrids for captive consumption, or connecting them to the main grid. Blockchain facilitates transparency in peer-to-peer energy trading.
Transportation
Sustainability in transportation manifests in the shift to EVs and hybrids. A lot of R&D and use of advanced technologies relating to battery technology, alternative fuels and charging infra are prompted globally by this industry. An engaging area is vehicle-to-grid tech, where EVs as storage units feed electricity back to the grid.
Carbon footprint
Leveraging AI for efficient carbon capture and its reuse for enhanced oil recovery in wells, its use as feedstock for producing methanol and such like chemicals, and in production of dry ice for extending shelf-life of food are advances. Advanced monitoring for leak detection is imperative. Other deployments include sensors for real-time data on emission and sequestration, satellite imagery for monitoring forest cover and land use change, and blockchain for traceability, important in offset markets.
Alternate fuels
AI also plays a crucial role in the transition to low- carbon fuels, like green hydrogen, e-fuels and biogas, by way of feedstock analysis and increased efficiency of electrolysis processes. IoT, in combination with AI algorithms, helps in promoting a circular economy, including new material analysis and life cycle assessments. Robots are now used for smart waste separation.
Climate resilience
With inputs from satellites, weather stations, observatories and ocean buoys, climate monitoring is about real-time information for conservation of our natural resources, for productive agriculture, safe mobility and preservation of life.
AI and other emerging tech allow us to tackle this energy transition paradox more effectively than ever before.
(The writer is founder, ThinkStreet)