Tag: energy

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Energy providers around the world have transformed their electricity generation profiles to include solar, wind, hydro, and geothermal to reduce the carbon intensity of their economies. Many countries have surpassed expectations by approaching or exceeding 50% of production stemming from renewable sources. Concurrently, the decarbonisation of the transportation sector and the growing use of air conditioning is putting upward pressure on electricity demand.

Energy providers are keen on leveraging AI in several areas (Figure 1).

The sudden evolution of the Energy sector is creating new complexity in the grid, which human operators will be unable to monitor and manage without the assistance of AI.

Predicting Supply and Shaping Demand

Output Forecasting. The primary inhibitor to the mass adoption of renewable energy is the issue of intermittency. Solar is affected by shorter winter days and cloud cover, while wind turbines are ineffective during periods of low or even high wind speed. If electricity supply does not precisely match demand, grid operators must fire up costly and carbon-intensive peaker plants to fill the gap or resort to rolling blackouts. AI is becoming a tool for generators and grid operators to forecast renewable output more accurately, insight which can, in turn, be used to shape demand.

Competitive Pricing. Wind farm operators are beginning to use adversarial AI to judge if publicly available data for velocity, pressure, and density obey turbulent flow physics and if not, to refine their forecasts. Equipped with more accurate projections, they can bid with greater certainty on day-ahead auctions rather than relying on less profitable spot prices. This consequently makes wind power more competitive with fossil-fuel-based generators and smoothens out hourly price variability.

Predicting Usage Patterns. Improved temperature and humidity forecasts can also be used by grid operators to carry out peak shaving – encouraging consumers to reduce consumption during high-load periods. By accurately predicting intervals of increased use of home heating or cooling, programs such as Rush Hour Rewards by Nest, allow distributors to remotely adjust thermostats during seasonal extremes for cash incentives. Advanced knowledge of these weather events can also give grid operators the chance to temporarily lift regulatory hurdles or conduct emergency maintenance to ensure maximum capacity is available.

Supply Orchestration. Home battery systems and electric vehicles are growing in acceptance and their storage capacity will eventually become an important piece of infrastructure for time-shifting supply to match demand. The increasing build out of solar PV has created an oversupply in the middle of the day while the rising adoption of home air conditioning creates a spike in demand after working hours, resulting in the so-called Duck Curve (see Figure 2).

By predicting periods of potential supply shortfall, distributors can increase prices to a level attractive enough to prompt battery owners to sell excess electricity rather than store it. The complexity inherent in such a distributed system is only manageable with machine learning to constantly optimise pricing and supply orchestration to simultaneously prevent excessive degradation of battery performance. This is already available for large scale battery operators, e.g. using Tesla Autobidder, and will become accessible to networks of home and eventually vehicle owners.

The Future

Optimising Renewable Generation with AI

Renewable energy sources continue to make efficiency gains due to engineering improvements. However, advances in AI will increase generation even further. Solar PV and solar concentrators that rotate on dual-axis trackers to follow the path of the sun must each operate individually according to their own precise position and the time of day and year. This must be balanced for efficiency to reduce excessive movement, which consumes a portion of electricity output. Neural networks and fuzzy logic can be applied to optimise rotation to maximise production while reducing power consumption for operation. Input variables can include position, time, temperature, and even sky colour. Similarly, wind turbines can dynamically alter their positions to maximise wind flow across the entire fleet rather than at an individual level. The large streams of data must be processed in real-time as wind variables change to have an immediate effect on output.

Stabilising the Super Grid

To improve resiliency and lessen the effects of renewable intermittency, there is a growing push towards increasing the interconnectivity of national grids. This ensures supply even when regional generators go offline or if sudden local peaks in demand occur. Moreover, interconnected grids help even out supply from renewable sources using the philosophy that it is always windy or sunny somewhere. For example, the proposed European super grid would take advantage of higher wind generation in northern countries in winter and in North Africa in the summer. Additionally, hydroelectric plants in the north could be modified to become pumped storage facilities powered by solar thermal plants in the south to supply all of Europe.

Not only will a super grid require investment in new infrastructure, such as high voltage direct current (HVDC) for efficient long-distance transmission but also in intelligent systems to manage the new complexity. The retirement of fossil-fuel generators and greater variability of renewable sources will require rethinking grid inertia and frequency control between countries. Measurement solutions, such as GridMetrix by Reactive, have been deployed by AEMO in Australia and National Grid in the UK to better monitor how inertia fluctuates as renewable sources ebb and flow. Once real-time data becomes available for analysis, infrastructure such as synchronous condensers and quick-response batteries can be automatically utilised to regulate frequency.

A Positive Outlook

Countries such as China, India, the US, Germany, and Spain have shown that it is possible to add large amounts of solar and wind generation capacity at a pace. The next chapter in the renewable revolution will be ensuring that this can be done at scale without disrupting the grid and AI will be a key component in managing the transition.

The combination of computational and biotechnology is accelerating the development of new cures, augmenting R&D and improving health outcomes. Deep Tech in healthcare has multiple applications from the manufacturing of affordable medical devices to redefining healthcare. Vibrosonic, has designed what they call a “contact lens for the ear” which can be directly placed on the eardrum. Unlike other hearing aids speakers are not used to transport sound through the ear canal but the eardrums are stimulated through electric impulses. A Singapore-based biotech company X Zell has patented a “liquid biopsy”- detecting cancer from a 10ml blood sample by measuring the presence of tumour-derived Circulating Endothelial Cells (tCEC) – which reduces the need for invasive cancer detection processes.

Food and Agriculture

Food crisis is a reality today with factors such as overpopulation, urbanisation, decreasing land per capita, extreme climates and so on impacting the food and agriculture industry immensely. Deep Tech companies are working to bring us sustainable food options and building climate resilience. Cell-based meat options are being researched globally, and companies such as foodtech start-up Shiok Meats is producing meat by harvesting cells from animals with a view to be environmentally friendly and to reduce the impact on biodiversity. In agriculture, Deep Tech companies are working on technologies to develop better farming methods to improve yield and precision sensors for weather forecasting. Examples such as UbiQD, that has worked on a greenhouse quantum dot film that improves crop quality by optimising sunlight spectrum for plants to improve production, show how Deep Tech will continue to transform the industry.

Environment and Energy

Deep Tech continues to come up with solutions that will help us in climate change mitigation, development of sustainable energy and energy efficiency. Innovations include Carbon Upcycling Technologies’ solution to capture and neutralise carbon dioxide. The carbon dioxide-enriched nanoparticles are used to make commercial construction materials and even consumer products such as jewellery. Celadyne Technologies has developed hydrogen fuel cells and electrolysers with nanocomposite membranes for a more efficient, cost-effective and eco-friendly energy source.

Advanced Computing

As technology evolves, there will be a need to support even greater compute and data-intensive tasks.  Deep Tech has impacted and will continue to impact advanced computing. The semiconductor and microchip industry is getting disrupted by cutting-edge global research, many by the top universities. MIT, for example, has developed a process called “remote epitaxy” to manufacture flexible chips. Potential use cases include VR-enabled contact lenses, electronic fabrics that respond to the weather, and other flexible electronics. Atom Computing is working on scalable quantum computing that will be able to scale millions of qubits using individual atoms – without scaling up the physical resources – in a single architecture.

Communication and Security

Communication and connectivity have seen a sea change in the last decade. As we wait for 5G to take off, this industry has become a playground for inventions. Aircision, is working on making 5G more accessible using its laser-based communications technology. The technology is developed to enable high-bandwidth communication and beam data between buildings thus aiming to eliminate the need for optical fibre installations and microwave. Another area that will keep getting a lot of attention from Deep Tech firms is communication security. Speqtral is working on space-based quantum networks to deliver secure encryption keys.

Examples such as these are an indication that Deep Tech is a reality today and has the potential to disrupt several industries and impact the lives of millions.

Where is Deep Tech Headed?

Government Interest in Deep Tech

Since Deep Tech is aimed at leveraging technology and engineering for sustainability and greater good, several countries are promoting Deep Tech R&D and initiatives. From emerging to mature economies, governments are supporting their Deep Tech industry. The New Zealand  Government has formed a Deep Tech Incubator program. The program is headed by the Government’s innovation agency to help Deep Tech companies and to create new tech jobs.

Singapore has created a strong Deep Tech ecosystem leveraging the funding ecosystem, the presence of global corporations, research and higher learning organisations and the Government that promotes innovation and entrepreneurship. Agencies such as SGInnovate and Enterprise Singapore are working with Deep Tech startups in advanced manufacturing, urban solutions and sustainability, and healthcare and biomedical sciences. Partnerships between universities, industry bodies and research organisations further fuel this ecosystem – the Critical Analytics for Manufacturing Personalised-Medicine (CAMP) is a partnership between Singapore-MIT Alliance for Research and Technology (SMART) and A*STAR for cell therapy manufacturing. The Government also funds and incentivises Deep Tech startups. The 2020 budget announced additional funding to support Deep Tech companies under the Start-up SG Equity scheme.

As global governments get serious about the quality of their citizens’ lives and sustainability goals, they will invest in Deep Tech research.

Challenges of the Deep Tech Industry

While Deep Tech has enormous potential, mainstream adoption is still some way off. There are some unique challenges that the industry faces today. Future uptake will depend heavily on how fast the industry can circumvent these challenges. The key challenges are:

• Securing Finances. Despite initiatives by several global governments, Deep Tech projects often find it difficult to secure funding. Very often the research duration can stretch without any real guarantee of success. Funding is likelier to go to organisations developing consumer products as the ROI are seen earlier and are easier to quantify, especially in the early stages.
• Identifying Market Opportunities. Researchers who develop Deep Tech solutions and products might not be able to identify opportunities to present their development from a marketing as well as an economic perspective. Very often these companies rely on other channels or third-party services for a proper marketing and planning strategy. This is where working with incubators or government bodies becomes crucial – countries that give that opportunity through a well-defined ecosystem, will lead the Deep Tech revolution.
• Scalable Development. Many Deep Tech innovations get stuck at the proof-of-concept stage – not because they are not innovative enough, but because they are not scalable to mass production. That requires the right infrastructure as well as a deep understanding of how the products and services can be commercialised.

There are several global companies trying to disrupt entire industries with their inventive offerings. We are witnessing some novel innovations in autonomous vehicles, foodtech, computer vision, AI, weather predictions, Clean Energy solutions  – the list continues – that we will benefit from in the future.

Let us know which Deep Tech companies have impressed you in the comments below.