Strategy and Best Practices, Energy
Article | July 27, 2022
Machine learning and artificial intelligence (AI) are two of the most commonly used commercial phrases these days. As a result, companies across sectors are searching for methods to include them in order to optimize and automate their key operations. The energy sector is no exception!
Indeed, throughout the years, renewable energy industries (wind, solar, hydro, nuclear) have substantially gained from the potential of machine learning. They were able to reduce their expenses, make better projections, and raise the rate of return on their portfolio. And this tendency is just going to gain momentum. If your company is in the energy industry or utilizes a lot of power, machine learning and AI can help you improve your business performance. But how precisely? Let's get started.
Ways in Which AI and Machine Learning are Changing Energy Sector
There are a few methods that machine learning and AI can be applied to positively improve the energy industry. Here are a few popular applications currently under development.
Predictive Maintenance
AI helps match energy output with demand and ensure power grid stability and resilience.In 2003, a low-hanging high-voltage electricity line hit an overgrown tree in Ohio, causing a widespread blackout. There was no power system alarm and no sign of the incident. The electric company didn't notice until three additional power lines failed. This carelessness ultimately brought down the whole grid. The 50 million-person blackout lasted two days. Eleven individuals died, and $6 billion was lost.
Predictive maintenance can be implemented using machine learning and IoT
Sensors gather operational time series data from electricity lines, equipment, and stations (data accompanied by a timestamp).
Machine learning algorithms can then forecast when a component will fail (or n-steps). It can also anticipate machinery's remaining usable life or future breakdown. These algorithms detect machine failure, eliminate blackouts or downtimes, improve maintenance procedures, and reduce maintenance expenses.
Grid Management
Grid management is a promising AI application in energy. Complex networks distribute electricity to users (also known as the power grid). Generation and demand must always match in the electrical system. Other issues, like blackouts and system breakdowns, can occur.
Despite being ancient, pumped hydroelectric storage is the most common way to store energy. It operates by moving water upwards and letting it fall into turbines. Renewable energy makes predicting the grid's power generation challenging. After all, it is affected by a variety of things, like sunlight and wind.
Demand Response
Large demand shifts can be expensive for nations that depend on renewable energy. As nations migrate to green energy, it's harder to adapt to demand fluctuations. Germany plans to use 80% renewable energy by 2050.
Countries such as Germany will encounter two major challenges Demand fluctuations: On some days or times of the year, power consumption soars (on Christmas, for example) Weather volatility: Without wind or clear skies, it might be hard to meet electrical demand. In both circumstances, more stations or fossil fuel-powered facilities must meet demand
Solving demand response issues
Many nations are partnering with businesses to examine weather forecasts, power demand, etc. Germany's EWeLiNE project forecasts wind and solar energy at a specific moment. This enables the government to use non-renewable energy to meet additional power demand.
They utilize enormous historical data sets to train machine learning algorithms, as well as data from wind turbines or solar panels, to properly balance supply and demand.
Closing Lines
AI increases the potential of humans. Several renewable energy producers are investing in artificial intelligence to boost their businesses.There are numerous uses of artificial intelligence in renewable energy. The fundamental purpose of AI integrated systems is to reduce forecasting issues and incorporate renewable energy into the central energy grid as effectively as possible. AI can also assist renewable energy providers in developing successful plans and policies based on present energy consumption and demand.
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Energy
Article | July 16, 2022
Cleaner energy resources are the dire need of the hour and this is a known fact. While scientists and experts across the planet are striving hard to reduce our reliance on fossil fuels, our energy needs have never faced a downfall- thanks to rapid industrialization and urbanization. Although renewable resources like solar, wind, and hydro-electric power are the most popular alternatives, these are seasonal energy sources and the energy production from the same will not be similar all around the year. The fluctuations in production hence cannot always meet the energy demand of the population, and this makes the renewable energy sources not completely reliable.
Solar Production v/s Demand of the same in a year
What and How H2 is produced?
Now, this is where Hydrogen- the first element of the periodic table comes to the spotlight with a solution. Being a gas, hydrogen fuel can very well cater to our energy needs and is produced from techniques including Thermochemical, Solar-Water splitting, electrolytic and biological processes. While the production of this cleaner energy source leaves a carbon footprint of about 830 million tonnes in the form of CO2 annually, the result being a zero-emission fuel is what makes H2’s future bright.
Storage of H2 – the million-dollar question:
Having almost cleared the need and methods of producing hydrogen fuel, we will be looking at an area that is usually not given much thought about and that is the storage of H2. As already mentioned, for time being let us consider hydrogen as an alternative to renewable resources which is utilized when the energy demand increases drastically. While producing the fuel in the nick of time is obviously undoable, sufficient storage of H2 anticipating the demand is the best choice. Like Natural Gas, Hydrogen is also compressed before storing to achieve lower volume and also because liquid hydrogen demands a 64% higher amount of energy for storage than its compressed gaseous counterpart.
Storage tanks v/s Geological landforms:
Compressed Hydrogen can be stored in surface storage vessels (like steel composite concrete vessels and in wind turbine towers) or in geological landforms like (salt caverns, depleted O&G reservoirs, and aquifers). Nevertheless, unlike the underground geological landforms which offer huge storage capacity owing to their sheer scale, the storage tanks which can range in size from a small bottle to a huge tank require high amounts of pressure to store an appreciable amount of H2 in it. Since these storage tanks are usually constructed on the surface, the pressure conditions in these tanks need to be artificially stimulated and thereby mount huge upfront costs when compared to their geological storage counterpart.
H2 storage prices in Geological Landforms v/s Storage Vessels (in $/kg)
The above is a table comparing the prices of Hydrogen storage in Geological landforms and Storage Vessels at different pressure conditions. It is visible from the table that it's about 218 times cheaper to store the same amount of hydrogen in Geological landforms than in storage vessels.
Is geological storage truly a better option?
Like any other storage option geological storage too has its pros and cons. From the erosion of pipelines to the tedious task of injecting the gas and maintaining it at apt pressure conditions, geological storage has its limitations. However, the important prerequisite is the availability of the suitable landform itself.
While most of the Depleted O&G Reservoirs have already met all the requirements for a suitable Underground Hydrogen Storage (UHS) system, the presence of unrecoverable remnant fluids in it makes it both a boon and a bane. This is because the presence of remnant fluids like oil and gas satisfies the cushion gas need for efficient storage of H2 in the reservoir, chances of contamination of H2 by the same is also high. This is the reason why Aquifers too aren’t favorable underground landforms when it comes to hydrogen storage.
Salt Caverns- the best UHS System?
The problem of Hydrogen contamination in Depleted Oil & Gas reservoirs and aquifers leaves us to the next big suitable subsurface landform- salt caverns. Unlike the other two landforms, the problem of contamination can be prevented in these dome-like structures formed due to the upliftment of salt deposits and it is also found that about 98% of its storage efficiency can be used to store Hydrogen here. The reason behind its relatively expensive nature when compared to its other two counterparts is due to the process of salt removing or leaching that must be done before storing to ensure that the contamination of the gas is unheard of at least here.
Suitable Conditions of UHS:
As per Stefan Iglauer, the maximum amount of H2 can be stored at a depth of 1100m beneath the Earth’s surface and the capacity gradually decreases up until 3700 m depth beyond which the wettability of the gas increases as it percolates through the rocks and hence cannot be permanently immobilized. Conclusively it is found that suitable landforms formed at 1km depth can store up to 2.0 Mt of H2. Comparing this 2 MT storage capacity of Salt Caverns with the currently available storage tanks which can store about 800 kg of H2 in it, it is visible that geological landforms have a clear upper hand at least when it comes to storage capacity.
Future of UHS:
With demands for Hydrogen fuel estimated to grow at 5.48 % annually and the need for a suitable storage system of the same at 5.8% annually, the field of Underground Hydrogen Storage systems indeed has a bright scope. Moreover, to meet the large-scale needs of Industries, there is an imminent need to level up the storage capacity of H2 and by exploring suitable geological landforms across the globe, the estimated industrial need of 1200 kT/ year in 2050 can be met.
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Solar+Storage, Strategy and Best Practices
Article | September 17, 2022
Covid-19. It’s everywhere, and it’s probably the reason that your food cupboards are unusually more stocked than usual, or the fact that you’re likely reading this blog from the confines of your own home, as opposed to at your office or during your daily commute. But, despite the impact to business, economies, daily life and public health, there’s one bittersweet development which we can all take away from the outbreak – and that’s the considerable reduction of global CO2 emissions, and a resurgence of hope that it is fully possible for us to slow the onset of climate change and preserve our planet for future generations.
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Energy
Article | July 15, 2022
As the worldwide use of artificial intelligence (AI) in the energy market is expected to reach $7.78 billion by 2024, with a CAGR of 22.49% from 2019 to 2024, it is easy to see why it's a popular topic on the minds of many leading brands in the energy sector, as well as investors looking to reap the future perks that AI could bring to the energy industry.
According to BIS Research, North America is expected to be the largest market for AI in energy through 2024. However, Asia-Pacific is expected to rise rapidly over the same time due to the rising need for more decentralized power production.
Investment Opportunities in AI-based Energy Industry: Economic Visibility
AI's economic viability and progress in the energy business can be attributed to numerous factors, including:
The desire to increase operational efficiency.
Increased interest in energy efficiency.
Decentralized electricity generation is being expanded.
Battery storage solutions are gaining popularity.
Since artificial intelligence has a wide range of applications, there are several investment opportunities in the energy industry.
Upstream Oil and Gas
Enhance efficiency and decrease downtime, which is critical for hydrocarbon companies owing to volatile oil prices and demand, to lessen the environmental implications of energy generation and consumption.
AI Chatbots
AI has the potential to enhance interactions between contact centers and consumers. Utilities that outsource to contact center providers can suffer significant fees. This is where AI, particularly when combined with natural language processing (NLP), can assist contact center operators by listening to conversations and automatically noting information in the appropriate apps, helping operators to make calls more reliable, effective, and satisfying to customers.
Smart Homes and Cities
AI integration benefits smart meters and smart energy management systems as well. Many residences and towns can utilize AI to collect real-time data and apply it in a number of ways to function more effectively and efficiently, enhancing sustainability while also making a living more comfortable and cities more accessible.
Monitoring Trends in Energy Generation and Consumption
Artificial intelligence is being utilized to assist energy companies and customers in recognizing and tracking patterns in energy generation and consumption. AI, for example, can predict the potential output of a certain wind or solar plant.
Closing Lines
Banking, finance, and trade are some of the suitable businesses that can profit. For example, AI and machine learning can be used in algorithmic trading, which involves utilizing computer programs to make trades in the energy business at speeds and frequencies that any human trader would consider inconceivable.
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