Energy is a vital resource for social, economic, and industrial growth of any country. Fossil fuels are the primary source of energy, but their overconsumption causes disastrous effects such as air pollution, habitat destruction, and a decline in human health. Fossil fuels are also non-renewable sources of energy, and their limited availability is depleting rapidly.
Renewable energy sources, such as solar and wind power, are the best options to protect the environment and solve the limited availability of fossil fuels. However, standalone renewable energy systems have limitations due to daily and seasonal changes to their production capabilities.
Hybrid energy systems offer an alternative, but their control requirements pose a significant challenge. Conventional control algorithms require an accurate mathematical model of the system, which is not always feasible. Artificial intelligence (AI) control, such as fuzzy logic control and neural network-based neuro-fuzzy inference systems, provides solutions to modeling problems and can identify the characteristic features of a system to achieve satisfactory performance.
What are hybrid solar PV-wind systems
A hybrid wind-PV power system is a renewable energy system that combines wind turbines and photovoltaic (PV) panels to generate electricity. The system can be designed to operate in grid-tied or off-grid modes, depending on the application.
To effectively control a hybrid wind-PV power system, several factors need to be considered. One critical factor is the variability of both wind and solar energy sources. Wind and solar energy generation can fluctuate significantly based on weather conditions, which can make it challenging to maintain a stable power output. Control strategies need to be in place to manage the variability of the energy sources and maintain a consistent power output.
Another critical factor is the need to balance energy generation between the two sources. Wind turbines and PV panels generate power at different times of the day. Wind turbines generate more power at night, and PV panels generate more power during the day. The system needs to be designed to balance the energy generation from both sources to ensure a stable and consistent power output.
To achieve effective control of a hybrid wind-PV power system, advanced control algorithms based on AI and machine learning (ML) can be used. These algorithms can optimize the power output of the system by predicting the energy generation from both sources and adjusting the system parameters to ensure a stable and consistent power output.
In addition, battery storage can be integrated into a hybrid wind-PV power system to provide energy storage and backup power. Battery storage systems can be controlled to optimize the charging and discharging of the batteries based on the energy generation from both sources, which can help to maintain a stable and consistent power output.
Overall, effective control of a hybrid wind-PV power system requires careful consideration of the system design and the use of advanced control algorithms to manage the variability of the energy sources and maintain a stable and consistent power output.
Controlling PV-Wind system using AI and ML
Advanced control algorithms based on AI and ML can help achieve effective control of a hybrid wind-PV power system. These algorithms can optimize the power output of the system by predicting the energy generation from both sources and adjusting the system parameters to ensure a stable and consistent power output.
One way AI and ML algorithms can be used is through predictive analytics. These algorithms can analyze large amounts of data from renewable energy sources, weather patterns, and energy usage patterns to predict future energy demand and supply. This information can then be used to optimize the operation of the hybrid wind-PV power system to match energy supply with demand, reducing waste and maximizing energy efficiency.
Another way AI and ML algorithms can be used is through control strategies that utilize data-driven models of the wind-PV system. For example, an AI-based control algorithm can be designed to predict the power output of a wind turbine based on weather conditions such as wind speed and direction. Similarly, a ML-based control algorithm can be designed to predict the power output of a PV panel based on factors such as solar irradiance and temperature.
These predictions can then be used to optimize the operation of the wind-PV system. For instance, if the predicted power output of the wind turbine is lower than expected due to low wind speeds, the control algorithm can adjust the system to compensate for the shortfall by increasing the output from the PV panels. Similarly, if the predicted power output of the PV panels is lower than expected due to cloud cover, the control algorithm can increase the output from the wind turbines.
The use of AI and ML algorithms in the control of hybrid wind-PV power systems can also improve fault detection and maintenance. For example, ML algorithms can be used to detect abnormalities in the power output of the system and identify potential equipment failures. This can help to reduce downtime and maintenance costs, ensuring that the system operates at peak efficiency.
Overall,AI and ML algorithms can improve the efficiency and reliability of renewable energy systems, making them a promising technology for the future.
AI techniques are becoming increasingly popular in the optimization and control of renewable power systems. This is because traditional control and optimization methods may not be able to handle the complexity and variability of renewable energy sources, such as solar and wind power.
As AI technology continues to develop and mature, it is likely that we will see even more advanced applications of AI in renewable energy systems in the future.
Source; Morocco World News.