Climate change is one of the biggest threats that humanity is currently facing. With these challenges which lie before the government and various energy solution providers, it has become imperative for them to provide a sustainable mode of renewable energy. This has also made renewable energy to be an alternative source of energy compared to fossil fuels. It is much safer and cleaner than conventional sources.
Artificial intelligence (AI) and machine learning (ML) have become important technology solutions as the industry is constantly looking for ways to cater to the rapidly increasing demand for clean, cheap, and reliable energy. These advanced technologies have the potential to analyze the past, optimize the present, and predict the future. This means that AI and ML have the potential to solve most of the challenges that currently prevail.
With technology making rapid advancements, the renewable energy sector has made significant progress in the last decade. However, there are a few challenges that still prevail that can be addressed with the help of AI and ML.
Challenges faced by the Renewable Energy Sector
There are numerous benefits when it comes to renewable energy sources. They are climate-friendly, minimize various pollution’s, the expenditure for infrastructure required is only one-time along with a couple of regular maintenance costs, and the cost of fuel is nil. Enhanced forecasting and scheduling of power resources become important for the renewable energy sector in order to effectively manage the grid.
Looking at the downside, the unpredictability of the weather is one of the main challenges when it comes to producing renewable energy.
Efficient technologies exist which can predict the weather conditions, but there are going to be sudden changes in the climate that can affect the energy flow. The supply chain of renewable energy is prone to such vulnerabilities. It needs to be smoothened enough to cope up with unexpected changes. Secondly, the recent developments in energy storage technology are still at a nascent stage and require thorough testing.
There is no doubt that the demand for renewable energy will increase in the near future which makes it more important for the investment in emerging technologies such as AI, ML, and IoT to improve productivity and overcome the shortfalls.
How Can AI Technology Help
The electric grid is one of the complex machines when it comes to renewable energy. Grids that are currently available face many challenges in accommodating the diversity of renewable energy because of the integral variability of wind and solar. This makes it necessary to have smart systems that can expand the integration of renewables into the existing grid and make renewable energy an equal player in the energy supply. Here’s how AI technology improves the reliability of renewable energy and modernizes the overall grid.
Smart, Centralized Control Centers
Devices and sensors of a grid are mostly interconnected to collect huge amounts of data. When AI is integrated into the system, this data can give new insights to the grid operators. It offers flexibility to the energy suppliers to cleverly adjust the supply with demand. The advanced load control systems can be installed with the equipment, such as industrial furnaces or large AC units, which can automatically switch off when the power supply is low. Intelligent storage units can also be adjusted based on the flow of supply. Along with this, making weather and load predictions with the help of smart sensors and advanced sensors which will improve the overall integration and efficiency of renewable energy.
Improved Integration of Microgrids
The way of handling distributed energy goes through a broad acceptance of microgrids. AI-powered control optimization can solve power quality issues and bottlenecks. Intelligent algorithms have the potential to provide real-time control over the dynamic nature of the grid below the substation autonomous controls. Algorithms that are available due to AI can also help to improve system optimization as new devices and new generation sources are integrated.
Improved Safety and Reliability
AI not only has the ability to manage the intermittency, but it can also provide improved safety, efficiency, and reliability. This helps companies understand the consumption patterns, identify the energy leakage and health of the devices.
Expand the Market
Integration AI will help suppliers expand the marketplace by introducing new service models and encouraging higher participation. Data related to energy collection can be analyzed with the help of AI. Such technologies can also provide insights regarding energy consumption. This helps suppliers to optimize the existing services and launch new service models. Retailers benefit from such insights to target new consumer markets.
Intelligent Energy Storage
Undoubtedly, storage technology is very helpful to solve problems related to the seasonal nature of solar and wind energy sources. But, due to the chaotic and periodic dynamics of these sources on both demand and supply side, battery hardware alone can’t bring true energy values. As the situation is complex, a simple solution cannot be the only savior. There needs to be a proper control production-consumption and energy storage. This can only be done when sophisticated software is available. This is where AI plays a vital role. By pairing RE with AI-driven storage we can make a change in paradigm.
What does the future hold?
According to a recent paper published by DNV GL, AI will increasingly automate operations in the coming years in the solar and wind industries and boost efficiencies across the renewable energy sector.
An increasing number of sensors are expected to be installed along with the increase in easier-to-use ML based tools, and the continuous expansion of data monitoring, processing and analytics capabilities to create new operating efficiencies.
Without a doubt, solar and wind industry stakeholders will witness AI benefits in several areas, including:
- Remote inspection is one of the main areas where robotics plays an important role, with additional and new benefits in maintenance and troubleshooting
- Crawling robots are built in a way that can get close to a structure’s surface giving rise to a new set of technologies such as microwave and ultrasonic transmitters and receivers, making it easier to reveal faults in materials
- Autonomous driving robots can optimize the supply chain process. This has the potential to build an entire optimize an onshore wind or solar farm
- To carry out effective and efficient scrutiny of solar panels, autonomous drones with real-time artificial intelligence supported analysis will become the primary tool.
- AI applications fast-tracking due diligence. This will reduce the time investment of planning and analysis that today requires many human hours.