Role of AI in Smart Grid Management
The whole idea of smart grid technology is switching to digital management of the traditional power grid system. Artificial Intelligence is taking the centre stage in this process by digitally observing and controlling the physical utilities of the grid. The creation of an intelligent smart grid lies in replacing manual operations with artificial intelligence and reaching the threshold of high efficiency, reliability and low cost throughout the process of energy from generation to consumption.
The application of AI in power load forecasting:
One of the main functions of AI in smart grid management is to match power production and load in real-time, which is highly required for the smooth functioning of the power grid on a daily basis. Power load forecasting means identification of the power demand beforehand. Forecasting is done for short, medium, and long terms ranging in time from a few minutes to more than a year. The Long and Short Term Model (LSTM) Network is the most commonly used model in load forecasting. The LSTM is suitable for processing and predicting important events with relatively long intervals and delays in time series.
Power generation forecast of renewable energy:
Though the generation of renewable energy power is increasingly important, the irregularity and volatility in the generation of such power and its integration with the grid affect the stability of the grid. Similar to power load forecasting LSTM is also used in predicting renewable energy generation for efficient management of the grid.
Fault diagnosis and protection of equipment in power system:
The AI aids the remote identification of power surges or other malfunctions in the grid which is otherwise manually identified, which would be too late for any saving to be done. Immediate identification of the fault in all types of conditions is possible only through AI supported power management system. The faults are quickly isolated ensuring the safety of the equipment, all done remotely.
Consumer electricity consumption behaviour analysis:
AI and its ability of machine learning can make use of the data from the smart meters of users for analysis of their electricity usage and classify them into different categories of users such as residential or industrial. These data are also processed to develop reasonable pricing charts. With enough data, the AI can detect any unaccounted power usage which prevents power theft, fluctuations, or surges due to climatic conditions, and detection of faulty lines in the system.
Power network Security:
Smart grid is a complicated system with real-time insight, information service, and dynamic control. The deep information flow and interaction will make the power management system susceptible to threats by external forces. Deep learning AI can automatically detect network attacks, detect malware and intrusion, and provide network security for power systems. With AI integration the probability of the power system being attacked is far less than that of a normal operation.
The challenges of applying AI to smart grids:
- ➤ Insufficient data: Accumulation of a large variety of data is vital for making efficient decisions. Research in AI applications based on small sample sizes is a problem that needs to be continuously studied.
- ➤ Infrastructure development: AI application operates by processing abundant data samples, advanced computing, and strong communication collaboration. For this kind of functioning, the development of relevant infrastructure resources such as cloud computing, big data, and other collaboration platforms needs to be improved.
- ➤ Deficiency of Power industry-specific algorithms: Inadequate algorithms for operating power management equipment is a major setback that needs immediate attention because algorithm is the basis of Al functionality. Most importantly, the special intelligent algorithms for power systems.
Artificial intelligence along with machine learning has the potential to fully transform the industry of power generation and distribution. When fully realised, their application can be used to achieve the desired outcomes. It has the potential to convert data to make actionable decisions targeted at providing for the customers in the most efficient way possible.