Solar+Storage, Strategy and Best Practices
Article | September 17, 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.
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 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.
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.
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.
Strategy and Best Practices, Energy
Article | July 27, 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 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.
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.
Strategy and Best Practices, Industry Updates
Article | August 16, 2022
The evolution of smart grid and the transformation in the power sector?
The concept of a Smart Grid has taken centre stage with an evolution of Solar, Wind energy sources, advanced technologies such as AI/ML , Energy storage , introduction of Electric vehicles, sensors that transmit real time data all of which make a smarter, more efficient electrical power grid possible.
In contrast the Existing grid is facing some complex challenges that include integrating renewable energy, Cyber security, high losses, unable to support large Electric vehicle penetration and empowering consumers to become power producers.
It is time for India to make this paradigm shift that touches right from Generation, Transmission, Distribution and consumption. So, the first step would be the installation of smart meters and Advanced Metering infrastructure which is a key component of the smart grid. The roll out of smart meters has already started and integrating other pieces into this smart meter value chain and other building blocks. This new metering system enables two-way flow of information between consumers and utilities and improve the overall grid operations, cost efficient and support large scale penetration of Electric vehicles. A major transformation is underway and utilities need to develop their roadmap for creating a modern Smart Grid.
Solar is seeing low tariffs and what one can interpret from these solar tariff trends?
In the last one year, more than 10GW worth of solar projects are auctioned and tariffs discovered are between Rs2 to Rs 2.5. These low tariffs are result of many factors that include aggressive bidding, entry of foreign players, and expectation that module prices will further fall. Also this Covid pandemic has shrink the economy, thus there are fewer tenders from the govt. with more developers chasing fewer tenders to stay in the race.
These low prices put enormous pressure on EPC companies and Module suppliers to deliver at these rock bottom prices. These bids take into account the low prices of Chinese imports, now with BCD (Basic custom duty) in force from April 2022 it will be challenging for power producers to continue executing projects at such low prices.
Another concern is the delay in signing PPA’s (Power purchase agreements) by Discoms. PPA’s once signed are valid for the entire term of PPA which is usually 25 years. But given the tender tariffs falling every few months, Discoms prefer to wait and delay the signing or renegotiate the existing PPA, dampening the investor confidence and threatening the viability of the Projects. In these circumstances the role of regulatory oversight increases to protect the interests of all the stakeholders. However, in the coming years technology improvements with addition of energy storage and better forecasting techniques, Solar would become the major source and also the cheapest source. So sunny days ahead of solar.
The decentralized solar and innovative business models and financing?
In the current system of centralized power system, a large power plant produces power, transmits, and distributes it among industries and homes. This process is inefficient as some of the electricity is lost in transmission and distribution.
A De-centralized solar is more efficient to generate and consume power locally. It also helps create small businesses and technicians to build and maintain these solar plants. Also as Solar and battery systems increase and become more economical Peer to Peer energy trading is possible where consumers become prosumers (both producers and consumers) and sell their excess power to their peers.
This next generation Energy Management and Peer-to-Peer Energy trading facilitates buy and sell orders just like share trading stock exchange. The Energy trading platform maps the buyers and sellers as per their bids and settles the trades. By introducing Block chain technology for energy trading further reduces the transaction costs. The possible business models would be Community based Solar plants where rooftops and open spaces could be used to generate power and trade. All of this result in less losses and brings the much needed dynamism in the distribution of energy.
Role of AI and data analytics in the energy sector?
The Power sector generates large amounts of data from various nodes on the grid and unfortunately most of this data go unanalysed due to lack of infrastructure and domain expertise. But now with the maturity in data management systems and two-way communication enabling real time data from various components of the grid giving latest and integrated snapshot of the entire power system, it is possible through the application of AI to provide services such as Fault detection, Predictive maintenance, Power quality Monitoring, and Renewable energy forecasting.
Many discoms are plagued by theft of power and Cyberattacks. The recent Cyber attack on Maharashtra power grid is an example that caused massive power outage in Mumbai last October plunging the city into darkness. By using the power of AI/ML, algorithms can be trained to detect any attack based on certain attributes. As soon as the attack is detected an alert is sent to the security engineers to bring the system to safety mode. In addition, Smart meters with pre-paid mechanism are expected to be deployed for remote meter reading and accurate billing thus preventing revenue loss.
AI/ML has the potential to cut energy waste, lower energy costs, and bring more operation efficiencies for the utilities.
Strategies in EV charging and integration with smart grid?
EV’s are promising solution to cut greenhouse gas emissions, reduce the cost of transportation and improving the health of citizens. The emerging business models are Public charging stations, third party owned operated charging station, and owner operated charging station.
However, the ground reality is far fewer EV’s are running on road due to higher cost, Range anxiety, and long charging times. So, there is need to work closely with all the stakeholders right from utilities, Regulatory bodies, Car manufacturers, charging station operators to expedite the process of EV related infrastructure and incentivize customers to adapt to EV’s rather than convention vehicles.
In your question you asked about integration with smart grid and this is a term that captures the shift from basic to smart charging. A smart grid is key to smart EV charging as large number of EV charging at same time can degrade grid performance causing voltage and frequency fluctuations and cause peak power demand or sudden drop in demand. With smart grid in place it is possible to do load balancing, adjust charging patterns and avoid peaking of power.
Also one more challenge is there are 3 competing standards and India should define its own standards and enable charging of any vehicle at any charging station. This interoperability is possible by developing standards for front–end and back-end communication and signalling process between Electric vehicles and charging stations and the grid that supplies the power. Smart grid is essential for large deployments of EV’s.
Investment opportunities and job creation in this transformation to clean power?
Covid has changed the entire investment paradigm and made all of us Environmentally conscious. This is wake up call to prioritize a more sustainable approach to investment in companies that are high on Environmental, Social and Governance score.
The recent momentum in ESG investment with more than 3,300 ESG funds is an indication that businesses that demonstrate business ethics, transparency, Sustainability benefit companies and investors and attract best talent too. The spectacular rise of share price of Tesla is a clear message from investors on clean energy and EV transportation. As the world is getting serious India has a catching up to do from the findings of Refinitiv on ESG.
As Asset managers, Pension funds, Oil and Gas companies evaluate their exposure to fossil based energy sources and switch towards clean energy this is going to create new Green jobs. These new Green jobs range from retrofitting homes with solar panels, providing home based charging stations, energy efficient appliances, Solid waste mgmt, e-waste mgmt. Similarly, Smart cities, Green buildings, greening of enterprises can be achieved by training the work force on these new concepts and driving investments towards job creation and sustainability.
In summary, power sector is in for a major transformation and utilities, industries need to tap the right talent to deal with this disruption and reap immense benefits.
Article | April 10, 2020
The need to reduce carbon emissions is real. In 2018, the International Panel on Climate Change (IPCC) reported that global emissions would need to reach net-zero (or carbon-neutral) by 2050 to prevent severe climate change impacts. Electricity is a major contributor—electricity generation was responsible for approximately 33% of total CO2 emissions in the U.S. in 2018. Electric utilities stand to play a critical role in reducing carbon emissions. Many are up to the task of decarbonizing their operations and supplying carbon-free or carbon-neutral energy to their customers.