Energy is one of the fastest-growing industries on the market today. With increasing energy consumption, the present energy sector is at the crossroads of a technical transition—the rise of renewable energy calls for efficient energy storage powered by robust technology. The energy storage market has grown at a staggering rate that has changed energy production, management, and consumption. As policymakers and government regulators seek efficient and cost-effective solutions, artificial intelligence (AI) emerges as the potential technology that offers flexibility and an innovative approach towards sustainable supply for energy. AI engines supersede the legacy systems for energy storage with their automated ability to control the charging cycle, failure prediction and maintenance, and data collection and turn them into insights for optimized control of operations and forecasting electricity generation as per the demand pricing.
Current State of Energy Storage
In recent years, a wide range of technologies has been introduced to store energy and save energy in large quantities at different intervals. The technologies such as batteries, thermal, mechanical storage, hydrogen, and pumped hydropower are constantly being used for managing our power supply. Despite these advances in technology, the energy sector has lagged in the efficient utilization of its energy resources. Moreover, the energy industry requires innovations that can achieve automation, longer battery lifespan, and power generation capacity planning for energy storage. The advent of AI technologies has gained traction across industries, and there could not be a better time for integrating AI in the core of the operations to address the energy storage challenges today.
Potential Contribution of AI in Energy Storage
Applying AI can immensely benefit the energy storage sector. The AI-enabled systems provide computing power and automation capabilities, including collecting and analyzing data, using data insights for a predictive outcome of possible outage and maintenance, and improving the longevity of batteries. Broadly, AI solutions have garnered interest in this industry and provided vital contributions in crucial energy storage areas.
Maintenance of Energy Storage Systems
The existing energy storage systems require monitoring and tracking the equipment's performance for preventive measures in case of failures. On the contrary, the performance monitoring of these storage systems can be a tricky process. Currently, network operation centers are responsible for collecting information about the site load, weather, and pricing factors. Besides, they monitor various additional components such as relays, load calculation meters, and data generated by solar and onsite generation.
As it is evident, an enormous amount of data is generated, making it complex for individuals to process multiple high-volume works. AI is well-suited for these requisites as it can contribute to reading and parsing a high volume of data at low computation cost and time. Similarly, AI systems can re-route the information to the appropriate location and create automated trouble alerts with tickets stating the potential repair areas. AI can also segregate high-priority tasks automatically and process the work accordingly. Using predictive AI solutions for maintenance ensures reduced downtime and prevents any critical damage, which increases the revenue for operators.
Real-Time Decision Making
Energy storage requires real-time solutions based on incoming data. The critical factors like the appropriate timing for charge and discharge of the energy storage systems ensure durability. Therefore, optimization is essential for achieving the maximum potential of the systems. As there is constant data generation, strategic planners should plan from studying the data and deciding on critical approaches to achieving reduced costs for energy consumption while generating revenue for the continuity of the business operations. The primary role of AI fits into providing real-time analytical insights from data and vital information onsite load, solar production, and onsite generation. AI can generate spikes when operating costs are on a larger scale and determine the charging cycles to attain the optimum balance. Furthermore, AI can provide accurate estimates of costs based on market trends.
Forecasting Electricity Generation Based on Demand
As we have seen, AI can provide cost estimates based on market data. Simultaneously, forecasting electricity generation is yet another central area where AI fits in remarkably well. AI considers the historical electricity generation data and data on weather conditions for providing an accurate rate of electricity generation required at different points of time. Such an approach is beneficial for operational efficiency and storing sufficient energy during peak hours and seasons.
Solar and Wind Forecasting for Renewable Energy
In the day and age of renewable energy, wind and solar forecasts enable us to tackle uncertainty periods to regenerate these resources. Smart-grids are responsible for maintaining the schedule for energy output delivery. However, the delivery of the energy output to the grid varies each day. Grid controllers need to know how much energy to use and how much to store on a given day. To tackle these challenges, AI uses weath