AI has a significant role to play in the control of distributed energy resources and automated energy trading. Here are some ways in which AI can help:
- Energy Forecasting: AI can be used to forecast the energy demand and supply for a specific area. By analysing historical data, weather patterns, and other variables, AI can predict how much energy will be needed and when. This information can be used to optimise the use of distributed energy resources.
- Energy Management: AI can be used to manage and control distributed energy resources, such as solar panels, wind turbines, and battery storage systems. By analysing data in real-time, AI can adjust the output of these resources to meet the needs of the grid.
- Automated Energy Trading: AI can be used to automate energy trading. By analysing market data, AI can determine when to buy and sell energy. This can be especially useful for distributed energy resources, which can generate excess energy that can be sold back to the grid.
- Demand Response: AI can be used to implement demand response programs, which encourage consumers to reduce their energy consumption during peak periods. By analysing data on consumer behaviour, AI can determine the most effective ways to incentivise consumers to reduce their energy usage.
- Predictive Maintenance: AI can be used to predict when equipment, such as transformers and generators, will fail. By analysing data from sensors, AI can detect patterns that indicate when equipment is likely to fail. This information can be used to schedule maintenance before a failure occurs, reducing downtime and maintenance costs.
Through AI we can optimise the use of distributed energy resources and improve the efficiency of energy trading. Automated AI driven energy trading can help ensure that the grid remains stable and reliable while also reducing costs and increasing the use of renewable energy sources.