Can artificial intelligence improve microgrid control?

Classical control techniques are not enough to support dynamic microgrid environments. Implementation of Artificial Intelligence (AI) techniques seems to be a promising solution to enhance the control and operation of microgrids in future smart grid networks.

How AI is used in microgrids?

AI gives the electric grid more reliability, intelligence and improved responsiveness. It is used for many purposes in microgrids such as integrating renewable energy sources, energy management and forecasting. Table 6 shows the AI techniques applied in the microgrids.

How can AI improve microgrid energy management?

Advanced data-driven energy management strategies based on deep reinforcement learning enhance MG stability and economy . Recent advances in microgrid energy management have increasingly relied on integrating AI techniques to enhance system reliability, optimize energy distribution, and reduce operational costs.

Is AI implementation progressing in microgrid control?

Implementation of AI techniques in microgrid controls is also gaining importance these days. A review on the progress of AI implementation appears in which focuses more on the microgrid stability issues. Authors in also have reviewed the progress on ANN implementation but were limited to a single microgrid only.

Can AI solve microgrid problems?

Then, the issues in microgrids and the potential AI solutions are analyzed. Firstly, for the microgrid control, we deem that the combination of traditional methods and DRL-based approaches is a promising tool in response to stochastic system dynamics and stability requirements.

How AI is used in the electric grid?

This machine analyzes the input values and accordingly generates the output. AI gives the electric grid more reliability, intelligence and improved responsiveness. It is used for many purposes in microgrids such as integrating renewable energy sources, energy management and forecasting.