The power sector is turning to digital solutions such as artificial intelligence (AI), machine learning (ML) and internet of things (IoT) to manage networks, re­store outages more quickly and oversee distributed energy generating sources in the most effective and affordable manner. The­re are several applications of these tech­nologies that help optimise processes, predict issues and save money. Further, po­wer companies can use these technologies to make predictions and manage network op­e­rations and maintenance.

Applications of AI, ML and IoT

  • Grid management: The management of multiple grid participants and grid balancing requires analysis and assessment of an abundance of data, which is processed more rapidly and effectively using AI. Another area where AI is crucial is smart grids. In addition to transmitting power, these intelligent grids transfer data coming from consumers, producers and storage facilities connected to the grid. AI assists in data analysis, evaluation and control. By identifying in­termittent demand, generation and transmission in real time, and creating suitable solutions, it helps stabilise the power network. Meanwhile, IoT, in co­m­bination with AI/ML, simulates the performance and intensity of components, in turn giving utilities early wa­rning about any possible breakdowns.
  • Energy efficiency: The cost of power in industrial units may be significantly reduced with the use of IoT-enabled en­ergy management systems. These systems are complemented by AI and ML, which provide analytical insights to enhance performance. The systems help managers in decision-making and operation simulation. In order to build a predictive model for forecasting, AI/ML algorithms can collect and analyse historical, daily data of different parameters, such as temperature and lighting, within an industrial unit. Energy suppliers can then allocate energy to the appropriate areas by gaining a better understanding of the total energy production and consumption.
  • Preventive maintenance: Damaged equipment or defects in the transmission and distribution (T&D) network pose a considerable risk and humans cannot predict every breakdown. In this regard, AI can help identify flaws such as corrosion and poor insulation and avoid failures by way of affordable and cost-effective solutions to detect defects. With the use of predictive analytics, operators can assess the condition of equipment and take preventive action before any catas­trophe. Energy suppliers can allocate and conserve their resources better, anticipate demand, and foresee problems with the use of AI-enabled predictive algorithms. Effective resource management helps in power and cost savings. Further, energy suppliers can get precise projections using predictive methods.
  • Grid security: The power grid is a complex system that is vulnerable to cyberattacks. AI and ML can be used to improve the security of energy grids by preventing cyberattacks. This involves using data analytics to identify patterns in energy data that may be indicative of a cyberattack. Once a cyberattack has been identified, AI and ML can be used to respond to the attack.
  • Power trading: As energy has to be delivered right away, energy trading is different from trading other commodities. By forecasting energy demand and giving traders access to real-time pricing data, AI and ML technologies can imp­ro­ve the efficiency of the energy trading market. Energy traders can then use this information to make better decisions about when to buy/sell energy.
  • Automatic meter reading: Automatic meter reading (AMR) systems enable large infrastructure set-ups to simply ga­th­er data, assess cost and identify the possibility of improving energy efficiency in the T&D system. AMR provides real-ti­me billing information. It is more accura­te than manual meter reading. In additi­on, it has the capacity to store data at distribution hubs connected to the utility’s networks. Energy consumption can also be tracked through AMR to he­lp in energy conservation and prevent energy thefts.
  • Distributed energy generation: Con­su­mers are now contributing to power generation, effectively acting as producers (prosumers). AI can help them in decision-making regarding the optimal time for distributed generation to contribute to the grid rather than draw from it. It can also assist conventional producers and system operators in balancing in­termittent renewables, distributed generation and new demand-side trends such as the growth of electric vehicles.
  • Customer interaction: Utilities are adopting AI/ML-enabled technologies such as chatbot and voicebot for enhancing customer engagement. By using AI and ML, companies can provide custo­mers with information that is specific to their needs.

Conclusion

AI, ML and IoT solutions are revolutionising the power sector with their applications and by helping overcome complex situations. The adoption of these techno­logies in the power sector is expected to increase in proportion with renewable en­ergy uptake in the grid. With improving technologies and declining costs, AI-, ML- and IoT-based solutions are likely to be the future of the power sector.