- A recent research shows Europe is one of the top regions for energy-related innovation.
- One of the key forces behind the switch to low-carbon energy is electric automobiles.
Data Bridge Market Research analyses that the electric vehicle charging stations market was valued at USD 6.97 billion in 2021 and is expected to reach USD 167.52 billion by 2029, registering a CAGR of 48.80% during the forecast period of 2022 to 2029. The growing popularity and use of electric vehicles have highlighted the need for charging infrastructure development. For instance, China, the United States, and Germany are spending heavily on electric vehicles (EV) charging infrastructure and research and development for faster and more efficient charging techniques. ABB (Switzerland), Shell plc (UK), ChargePoint (US), Tesla (US), BYD (China), bp Chargemaster (UK), Webasto Thermo & Comfort (Germany), Schneider Electric (France), Blink Charging Co. (US), Groupe Renault (France), Phihong USA Corp. (US) among many others are some of the major players operating in the market.
To know more about the study, visit: https://www.databridgemarketresearch.com/reports/global-electric-vehicle-charging-stations-market
One of the essential steps in solving the problems caused by the climate catastrophe is the transition to low carbon energy (LCE). The Paris Climate Agreement's temperature limits can be exceeded if emissions are not lowered, and the use of cleaner energy is not expanded. According to the second study on the development of the technologies required to support the switch to greener forms of energy, which was released by the European Patent Office (EPO) and the International Energy Agency (IEA), this is the case. The EPO and IEA have combed through international patent databases to find patterns in innovation, tallying cases when patents have been filed in multiple offices, known as international patent families, to gauge the progress accomplished so far (IPFs). According to the paper, "This patent data offers early indicators of technological advancements that are certain to impact the economy and can thus illustrate how innovation is fueling the energy transition."
Fig.1: Global Growth of Low Carbon Energy
Source: European Patent Office
Between 2014 and 2016, there was a slowdown in the expansion of IPFs for green energy. But according to the EPO/IEA report, it is once again increasing. Additionally, the rise in LCE-related patents coincides with a decrease in the use of fossil fuels.
Artificial intelligence (AI), as it does in every industry, is revolutionizing the energy and utility industries. In order to ensure that power is supplied when and where it is needed with the least amount of waste, it is used to estimate demand and control the distribution of resources. This is crucial for the renewable energy sector because renewable energy is frequently not suitable for long-term storage and must be used as soon as possible once it is produced. According to the World Economic Forum, AI will be crucial to the global switch to renewable energy. An increase in efficiency will result from more precise supply and demand predictions.
Decentralized models of power generation and distribution are also replacing centralized ones. In these models, more power is produced by localized, smaller power grids (such as solar farms), and coordinating the integration of these networks necessitates sophisticated AI algorithms. The plan is to build an "intelligent coordinating layer" that will lie between the power infrastructure and the buildings where people and things use electricity.
In 2022, we can anticipate more innovation from startups utilizing AI in fresh ways. As an illustration, Likewatt in Germany developed Optiwize, a service that estimates carbon dioxide emissions and power consumption to help consumers monitor the effects of their power consumption in real-time and make more informed choices about their energy supplies. To increase efficiency in producing renewable energy, other businesses are creating technology for predictive maintenance. A more integrated and electrified energy system with increased interaction between the power, transportation, industry, and construction sectors results from attempts to decarbonize the world's energy system. High degrees of decentralization in the power sector are also being caused by the effort to decarbonize the energy supply. In order to manage this increasingly complex system and optimize it for the lowest greenhouse gas emissions, it will require considerably higher levels of cooperation and adaptability from all sector actors, including consumers.
With potential applications ranging from optimizing and effectively integrating variable renewable energy resources into the power grid, to supporting a proactive and autonomous electricity distribution system, to opening up new revenue streams for demand-side flexibility, AI has a significant potential to support and accelerate a reliable and least-cost energy transition. The hunt for high-performance materials that underpin the newest sustainable energy and storage technologies may benefit significantly from using AI. However, despite its potential, AI is occasionally used in the energy sector, mainly in experimental programmes for proactive asset maintenance. While effective, AI has a much higher potential to speed up the worldwide energy transition than is now appreciated. Below is the discussion as to how AI will be impacting the energy sector via a wide range of applications:
Fig.2: Top Applications of AI in the Energy Industry
- Smart Grids- To become "smart," grids can now be connected to sensors, data analytics tools, energy storage systems, energy management platforms, and other energy technology. Energy providers can use smart grids to gather data on energy consumption from every grid device and create energy-efficiency projects for their clients. Additionally, it enables near-real-time monitoring of energy use and flows by energy firms. Then, with automated demand response systems that may cut off energy during peak hours, energy firms can minimize energy usage. As a consequence, both households and energy providers can save energy. A microgrid is a small electricity grid that can function independently from the main grid. AI and machine learning are used by microgrid control systems to optimize energy use and control energy flow. Because they can offer energy security during emergencies and make it simpler to integrate renewable energy sources into the grid than traditional energy networks, microgrids are growing in popularity.
- Grid Security and Management- AI is used to manage energy flows inside and between buildings, businesses, storage batteries, renewable energy sources, microgrids, and the main power grid in order to optimize energy systems. This lessens energy waste while raising consumer awareness of energy use. Even though intermittent renewable energy sources such as wind and solar are growing in popularity. As a result, these energy sources are not always available when needed. Since the energy grid must manage the energy in real-time as it is created, this poses a challenge. Energy firms can predict when renewable electricity will be available and manage energy grids accordingly with the help of AI and machine learning. Robots are also employed for energy installations, grid upkeep, and keeping track of energy production and consumption. In order to repair pipelines, wind turbines, and other energy infrastructure, robots can be utilized. Energy firms can further increase efficiency and cut costs by automating these processes. A sophisticated system such as the electrical grid is open to hackers. By thwarting cyberattacks before they occur, AI and machine learning can increase the security of electricity infrastructures. To do this, data analytics will be used to find trends in energy data that could be signs of a cyberattack. AI and machine learning can be used to react to a cyberattack once it has been detected.
- Power Theft Detection- Electricity theft and fraud cost the energy and utility sector up to $96 billion annually, with up to $6 billion in losses occurring in the United States alone. The illicit drawing of energy from the grid is known as power theft. The deliberate distortion of energy data or energy usage is known as energy fraud. These anomalies can be automatically found and flagged for resolution by energy firms using AI and machine learning. Energy firms can do this to safeguard their resources, cut down on energy waste, and make financial savings.
- Improved and Increased Production- The energy sector is also using AI and machine learning to increase production. For instance, machine learning algorithms are used by oil and gas corporations to better site wells and boost production. These businesses can decide where to drill for oil and gas more effectively by analyzing data obtained from seismic surveys and other sources. This will improve energy efficiency and result in a cleaner, a more effective energy system that will be simpler for energy providers to manage.
- Energy Storage and Predictive Analytics- By 2030, the market for energy storage is expected to have increased 20-fold. Smart energy storage technologies can be included into the energy grid to improve the effectiveness of energy management. Electricity businesses can now deliver energy when it is required even if their current energy supply is insufficient by using energy storage to build virtual power plants. This lessens the requirement for energy corporations to construct brand-new power plants. Future changes in energy demand can be predicted using predictive analytics. The appropriate infrastructure can then be constructed in order to plan for the future and supply energy needs. Energy businesses can also forecast when a machine or piece of equipment is most likely to malfunction by employing predictive analytics. This not only aids in preventing unanticipated outages but also helps businesses save money by enabling them to prepare for the replacement of expensive and essential energy assets and steer clear of unforeseen maintenance tasks.
- Customer Engagement- The energy sector is beginning to embrace AI and machine learning for client interaction. Energy firms can give clients information that is tailored to their requirements by utilizing AI and machine learning. This entails analyzing client data to understand their energy usage and then providing them with information on how to change their usage habits to consume less energy.
- Trading Energy- Because energy must be given right away, trading energy differs from other commodities. Energy traders face a challenge because of this, but there is also a chance because the energy markets are getting more liquid. By forecasting energy demand and giving traders access to real-time price data, AI and machine learning can be utilized to improve the efficiency of the energy trading market. Energy traders can then use this information to make more informed choices about when to buy and sell energy. Power purchase agreements (PPAs), a financial contract between energy purchasers and sellers, have been developed using blockchain technology. These contracts are more effective thanks to blockchain technology because it speeds up transactions, cost less to use than conventional PPA platforms, and is based on a very secure platform.
Renewable energy connector market is expected to grow at rate of 6.10% the forecast period of 2021 to 2028. Data Bridge Market Research report on renewable energy connector market provides analysis and insights regarding factors such as growing adoption of renewable energy sources. High installation costs and depletion of natural resources are acting as market restraints for renewable energy connectors in the abovementioned forecast period. Growing levels of global warming and rapid increase in population will become the biggest challenge in the growth of the renewable energy connector market in the above-mentioned forecast period. Renewable energy connector market is segmented on the basis of types, source of energy, application and end user. Asia-Pacific will dominate the renewable energy connector market due to increasing energy reforms in the region along with the growing number of distribution channel, while North America will expect to grow in the forecast period of 2021-2028 due to the prevalence of favourable policies and growing renewable portfolio standards.
To know more about the study, visit: https://www.databridgemarketresearch.com/reports/global-renewable-energy-connector-market
How will AI Accelerate the Pace of Energy Transition?
Unambiguously stated in the new IPCC assessment, more action is urgently required to prevent catastrophic long-term climate impacts. Fossil fuels still provide more than 80% of the world's energy, thus any initiative must center on the energy sector. Fortunately, the energy system is already changing; renewable energy production is expanding quickly due to declining costs and rising investor interest. However, there isn't much time left and the scale and cost of decarbonizing the entire energy system are still enormous. The majority of the energy industry's transitional efforts have, up to this point, been concentrated on hardware: new low-carbon infrastructure that will take the place of legacy carbon-intensive systems. Another crucial instrument for the shift, next-generation digital technologies, particularly artificial intelligence, have received very little attention and funding (AI). These potent technologies have the potential to accelerate the energy transition by being adopted at scales faster than new hardware solutions. Three major trends drive the potential for AI to speed up the energy transition:
- Historic decarbonization processes are only getting started in energy-intensive industries, including power, transportation, heavy industry, and buildings, thanks to rising public pressure for swift CO2 emission reductions. These transformations are massive in scope. According to BloombergNEF, between $92 trillion and $173 trillion in infrastructure investments will be needed to achieve net-zero emissions by the year 2050, just in the energy sector. Therefore, even modest increases in clean energy and low-carbon industrial flexibility, efficiency, or capacity can result in trillions of dollars in value and savings.
- The power sector is evolving into the main pillar of the world's energy supply as electricity supports more industries and applications. To ensure that power networks can be managed safely and reliably, increasing the deployment of renewable energy will mean that more power will be supplied by sporadic sources (such solar and wind), increasing the necessity for forecasting, coordination, and flexible consumption.
- The rapid expansion of distributed power generation, distributed storage, and improved demand-response capabilities is driven by the shift to low-carbon energy systems. These capabilities must be coordinated and integrated through more networked, transactional power grids.
The energy system and energy-intensive sectors face enormous strategic and operational hurdles in navigating these trends. AI can assist energy-system stakeholders in identifying patterns and insights in data, learning from experience and improving system performance over time, and predicting and modelling potential outcomes of complex, multivariate situations by establishing an intelligent coordination layer across the generation, transmission, and use of energy. Multiple areas of the energy transition are already seeing tangible benefits from AI, including forecasting renewable energy, grid operations and optimization, distributed energy assets and demand-side management coordination, and materials innovation and discovery. Although the use of AI in the energy sector has so far shown promise, there hasn't been much innovation or widespread acceptance. This offers a fantastic chance to hasten the transition to the future energy system that we need—one that is emission-free, extremely efficient, and linked. The ability of AI to speed up the global energy transition is much larger than previously thought, but this potential can only be realized if industry-wide AI innovation, adoption, and collaboration are increased.
How is AI Key to Renewable Energy Grid Resilience?
- In order to manage decentralized grids throughout the global switch to renewable energy, artificial intelligence (AI) technology will be required
- AI can optimize energy use and storage to lower costs and balance the needs for electricity supply and demand in real-time
- Technology governance will be required to secure resilient electrical sources, promote innovation, and democratize access
In order to solve today's challenges using technology from the past, calls for government spending on grid infrastructure to modernize long transmission lines from a centralized power supply source have been made. A superior, more progressive substitute already exists Artificial Intelligence (AI) that makes use of distributed renewable energy sources. Therefore, AI is key to renewable energy promotion in two ways:
Fig.3: AI's Assistance in Promoting Renewable Energy
- Increased Complexity in Renewable Energy- More energy will be generated from distributed, renewable sources as the world becomes more electrified. Consider batteries, private solar panels, wind farms, and microgrids. Even if they are advantageous for sustainability, these will complicate energy infrastructure worldwide. A delicate balancing act will be necessary to match supply and demand without bringing the grid to its knees over the next 10 to 15 years as a result of the increasing adoption of electric vehicles, the electrification of heating systems, and the proliferation of distributed energy resources (DERs) like wind turbines and solar panels. Use Australia as an illustration. By 2030 and 2050, 30% and 60% of the nation's residential, commercial, and industrial structures are expected to use solar energy. Similar situations are occurring worldwide as more commercial, governmental, and residential consumers produce their own energy using solar panels, store it in batteries for use in electric vehicles, or return it to the grid. Our projections show that by 2030, there will be 89 million energy storage devices on the grid in Europe, up from the current estimate of 36 million (see picture below). Electric grids may become chaotic if millions of individual gadgets post and download electricity. In other words, utilities will need to change their business models since the reliance on a single utility to produce and transmit electricity is dwindling. Soon, they won't be the only energy source; instead, they'll be required to keep the grid balanced by transferring electrons from various sources and storage systems to supply energy where it's needed second by second efficiently.
- AI to Balance Millions of Grids- Decentralized energy sources can transfer any extra electricity they generate to the grid using AI software, and utilities can route that electricity where it is required. Similar to energy storage, which can keep extra energy when demand is low in homes, offices, cars, and other structures, AI can use that energy when generation is insufficient or impossible. There are many moving pieces in that system; thus, coordination, forecasting, and optimization are needed to maintain grid stability. A utility is like a conductor maintaining the orchestra in time as AI composes the symphony in real-time if you imagine DERs as individual musicians. As a result, an AI-based system can transform the game. A grid that is more resilient and flexible when unforeseen events occur is the outcome of switching from an infrastructure-heavy system to one that is centred on AI. Forecasting and control are now possible in seconds rather than days.
With regard to decentralized energy resources, utilities, decision-makers, and regulatory agencies must begin considering their respective roles. The management and coordination of the patchwork of distributed energy producers will be essential. Utilities can take the lead in this situation as they deal with a decreasing number of customers buying electricity as more houses and businesses start producing their own energy owing to rooftop solar panels and similar technologies. There is no time to waste because climate change will continue to bring more extreme weather to the world. The current economic condition and drawn-out political discussions like the one anticipated in the US are likely to drag down necessary investments. The best course of action is not to invest in centralized grids with their network of lengthy cables and transformers; rather, governments should make plans for a grid where communities and buildings produce their own electricity, which is then managed in real-time by software. Public finance of the production of renewable energy as well as incentives for more dispersed energy generation in private industry and houses, should be considered by policymakers. And in order to guarantee interoperability, transparency, and fair access throughout the energy environment, we need globally approved governance of AI software.
Conclusion
A proactive and cooperative approach to AI-related technology governance would be advantageous for the energy sector. The upcoming years will be important for promoting innovation in this area and democratizing access to innovative low-carbon technologies throughout the energy system. If not previously accepted, the industry must implement common data standards as a condition for this and digitization more generally. Increased cooperation between actors in the energy industry may take the form of joint R&D projects, sharing best practice techniques for putting AI concepts into effect, and presenting use examples. Collaboration could also promote trust among AI technology creators, consumers, regulators, and other stakeholders interacting with AI systems. Grid regulators and operators must consider the potential of a variety of digital technologies (such as machine learning, quantum computing, blockchain technology, among others) to enhance the way grids are operated as the management and operation of grids become more complex, particularly on the distribution grid level. The need to rethink grid management and an opportunity to develop new and more decentralized designs for grid access, operation and management decisions arise as the power system decarbonizes and decentralizes. The traditional manual command-and-control management method (with a central system operator) should be replaced with technology-enabled decentralized decision-making, enabling quicker decision-making and automatically adding smaller distributed assets to the grid (using, for instance, blockchain, digital identity, and smart contracts). Governments could order or offer incentives to public and industry bodies to manage and fund central databases of industrial data as part of this equitable dissemination of data. These datasets would allow for the training of AI algorithms and could possibly lessen algorithm biases that are frequently brought on by poor quality or sparse data.
Rise in the demand for power efficient and durable systems has led to the rise in demand for energy harvesting systems. Data Bridge Market Research analyses that the energy harvesting system market will exhibit a CAGR of 10.04% for the forecast period of 2021-2028. This means that the current market value will rise to USD 1,042.5 million by 2028. An energy harvesting system is the technology that converts the energy from the environment in to usable electric power. This system extracts small amounts of energy from the environment that otherwise have been lost in the form of heat, light, sound or vibration. North America dominates the market owing to the increased adoption and application of energy harvesting systems in buildings and home appliances. Growth in the industrial and automotive sector too has fueled up the growth of the market across countries in this region. The U.S. is the largest contributor here.
To know more about the study, visit: https://www.databridgemarketresearch.com/reports/global-energy-harvesting-system-market
