Energy Tech Podcast: Technology and Efficiency in a Revolutionised Energy Sector
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Using energy in a smart and efficient way is key to reducing greenhouse gas emissions to manageable levels and achieving Net Zero by 2050. Advancements in technology in the field of machine learning, artificial intelligence, the Internet of Things and blockchain, have a huge potential to eliminate inefficiencies in energy use by both businesses and individuals.
Many countries around the world have committed to achieve Net Zero by 2050 (or 2045 if one thinks of Sweden). This transition to sustainably sourced energy is imperative to achieving stable economy and reducing greenhouse gas (GHG) emissions to manageable levels. However, such a transition has to be just and equitable in reconciling many competing interests. The world faces the task of bringing electricity to 770 million people who are currently denied access to it while, at the same time, reducing greenhouse gases produced in its generation, distribution and end use. A task of unsurmountable proportions, it may seem.
Using energy in a smart and efficient way is key to reducing GHG emissions. The ways in which we use energy to light our offices and homes, to power our appliances and to entertain our friends is in itself a huge source of energy waste. Every light left on in an empty room, every degree of heating for an empty house, every television or computer left on when not in use, creates demand for energy which is still largely produced by GHG-emitting generation facilities. A 2020 study by Green Alliance shows that energy lost by the City of London's offices is equivalent to energy needed to power more than 6500 homes – roughly all the houses in the London Borough of Kingston upon Thames. The carbon impact of this energy loss is equal to the yearly carbon emissions of 46,000 cars, and costs the City's businesses £35 million a year. There is no doubt that energy efficiency is therefore an essential component in the transition to the Net Zero economy.
There have been numerous government campaigns to reduce energy waste and energy efficiency, but human nature is difficult to change. However, advancements in technology in the field of machine learning (ML), artificial intelligence (AI), Internet of Things (IoT) and blockchain, have a huge potential to eliminate inefficiencies in energy use by both businesses and individuals. Cheap smart sensors can be installed in offices and homes to feed information on energy use directly into optimising algorithms, which could then automate the energy supply according the highs and lows of the demand. With enough structured data, algorithms could in time achieve a perfect match between the energy supply and demand. However, even if this supply-demand equilibrium could not be achieved (due to one-off unpredictable events – think of a day-long conference in the office, or late-night party at home), surplus energy supplied when the demand is low could be stored in batteries on site and used when the occasional higher demand exceeds the energy pre-allocated by the optimising algorithm on the basis of historic use.
A great practical example of how technology can be used to achieve energy efficiency is Powerhouse Brattørkaia in Trondheim Norway, the northernmost energy-positive office built to date. The building is renowned for using a variety of technologies to reduce energy consumption in its daily operations. Solar panels on its roof and the upper part of its facade generate around 500,000 kWh of electricity a year to supply the office with all the electricity it needs. The surplus, which is reported to be half of all the solar energy generated, is stored in batteries and used to supply electricity during winter months, when sunlight is less abundant, or power electric vehicles on site. This operating model effectively turns the building into a power plant that generates electricity to meet not only its own energy demand year-round, but also energy demand of houses and cars in its vicinity.
Environmental benefits of smart energy use are clear - digitally enabled energy efficiency is an inexpensive way to optimise energy demand and eliminate the amount of GHG emissions generated from wasted energy. From investors' point of view, data that enable algorithms to optimise energy supply can also inform the design of offices and homes of future and result in redirecting investment flows from inefficient network infrastructure to power generation model informed by historic patterns of energy use. For businesses themselves, savings would be clear from a glimpse at their energy bills. For instance, the UK government’s 2030 energy efficiency target could save small and medium enterprises (SMEs) around £2.7 billion a year by 2030, and £6 billion a year for UK business as a whole.
Equally significant, especially for those 770 million people currently with no access to electricity, is the fact that efficient energy use can promote economic growth – in the UK alone energy efficiency has been directly responsible for a quarter of the country's economic growth since 1971. In this regard, one can envisage a completely new energy market in countries with no national grid to keep a central grip on energy supply. IoT, which refers to a collection of network-enabled devices connected to one another via Wi-Fi or Bluetooth, may serve to connect smart appliances, like refrigerators, home security systems and printers, with a power source, such as solar panels placed on a roof of a house, and provide it with data on the intensity of energy use in a defined period of time. After a period of learning, the algorithm learns how much energy needs to be supplied to a house for the inhabitants to be able to live comfortable lives according to their individualised needs. Energy generated by the house owner but not needed for running of the household can be then sold on to their neighbours or local business owners, ideally via self-executing smart contract. Under this model, energy users become energy suppliers in a decentralised and decarbonized energy market.
However, all revolutionary solutions come with their own challenges and intelligent technology is no exception. Smart algorithms need a vast quantity of data to be able to efficiently optimise energy use in a single office or household. Ideally, this could be achieved by feeding publicly available structured data to the model. Yet such data would not reflect individual energy use habits of the end user, whom the algorithm should eventually serve. Therefore, in most cases, the algorithm will need to access data of individual users to learn about their personal habits and preference. Such data will need to be stored somewhere in a cloud or a centralised system. As any data system, the data on individual energy use would be susceptible to cyberattacks, which can result in passing the control over one's home and appliances in it from a smart algorithm to an illicit hacker willing to manipulate the system to their own advantage. Moreover, such data is likely to be "personal data" within the General Data Protection Regulation (GDPR) and hence heavily regulated – for example with restrictions on where the data can be sent and processed and who can access it.
In conclusion, AI, MI and IoT offer a quantum leap in demand-side energy management and efficiency, which are crucial components of the world's goal of achieving Net Zero. The adoption of such technology presents both technical and legal obstacles, but nothing which is unachievable. The key, as ever, is identifying the issues and devising solutions – and doing so has never been as important as now.