Be smart – Using artificial intelligence and blockchain to decarbonize the energy sector
Climate change and the need for decarbonization
There is no doubt that we are currently in the midst of a climate crisis. Once-in-a-hundred-year events occur every two years; from bushfires to floods, unprecedented weather events are wreaking havoc around the world. According to the “Sixth Assessment Report of the Intergovernmental Panel on Climate Change”, climate change, including increased frequency and intensity of extreme weather events, has resulted in (among other things) disruption of infrastructure (including transport, water, sanitation and energy systems) as well as marked negative economic impacts.
Figure 1 – Observed impacts of climate change on human systems (IPCC 2022)
The 2019 IPCC Special Report on the 1.5°C Pathway called for a radical reduction in fossil fuel production, stating that to prevent global temperatures from rising 1.5°C above pre-industrial temperatures, CO2 emissions are expected to decline by around 45% from 2010 levels by 2030, reaching net zero around 2050. Whereas CO2 emissions decreased in 2020 (due to COVID-19 enforced lockdown)emissions rebounded in 2021, returning to near 2019 emissions levels. Based on current trends, we are far from meeting our emissions reduction targets.
AI, blockchain and energy
Despite the pressing need to move away from fossil fuels, the stark reality is that almost 85% of the world’s energy is generated by using these same fuels (accounting for about 40% of all global greenhouse gas emissions greenhouse (GHG)). In addition, between 2010 and 2017, energy production increased by 70%and is expected to increase by the same amount by 2050. As such, we must find a way to offset our reliance on fossil fuels while transitioning in a way that does not place an undue burden on global infrastructure.
Energy networks are a network of synchronized electricity suppliers and consumers, linked by transmission and distribution lines and operated by control centers. The power generated must equal the power consumed at any given time – a feature that requires complex data analysis to forecast power demand in order to plan the generation of the required amount of power. Established over the last century, power grids have certain inefficiencies, which can lead to loss of up to 30% of generated powerdepending on the country.
AI and the energy sector
A study conducted by Boston Consulting Group found that the use of artificial intelligence (AI) can achieve 5% to 10% of the necessary reduction in greenhouse gas emissions, while delivering $1.3 trillion to $2.6 trillion in value generated by additional revenue and cost savings by 2030 (and potentially more savings, if carbon offset prices increase). Specifically, AI can help move away from reliance on fossil fuels by reconfiguring the way we design, operate, and optimize energy systems.
Using machine learning (a process that uses data and algorithms to mimic the way humans learn, using past experiences to improve future accuracy), AI can use historical data, weather patterns ( in the case of renewables), consumer demand and market prices to create more accurate forecasts, allowing for more efficient electricity planning and longer term system planning. Additionally, this would happen in real-time, allowing the system to automatically correct anomalies and create a smoother, more interactive energy system. This would lead to a sustainable reduction in the losses generated, leading to less necessary energy production and a decrease in GHG emissions in the sector.
Blockchain works by creating a system of trust between users on a shared network. It is run by a peer-to-peer (P2P) network, where discrete nodes collectively adhere to a certain protocol (i.e. distributed consensus algorithm) to communicate and validate new blocks. This technology eliminates the need for a central intermediary to validate transactions, saving time and money, while providing a high level of transparency and security.
Blockchain can be used in the energy sector to provide decentralized energy trading, smart metering and billing, and improved grid management. The launch of Ethereum in 2015 enabled the use of smart contracts in blockchain technology, opening up the possibility of P2P energy exchanges. Coupled with AI-based prediction, this would redistribute energy on a P2P basis, reducing the amount of wasted energy.
Potential privacy issues
In Australia, privacy matters are primarily regulated by the Privacy Act 1988 (Cth) (Privacy Act), with the main obligations arising from the 13 Australian Privacy Principles (APPs). The use of blockchain technology and machine learning algorithms to optimize the energy sector in itself raises privacy concerns.
An example of tension is the need for AI systems to collect and process data. In order to recognize more sophisticated and detailed patterns to make more informed predictions, an AI system should collect as much data as possible about its users. However, APP 3 requires organizations not to collect personal information unless the information is reasonably necessary for one or more of the entity’s functions. That said, given that data collection would be reasonably necessary for the operation of the AI, provided the personal information consents obtained were appropriately drafted, we do not believe this would preclude the use of AI for rationalizing the efficiency of the energy sector.
Additionally, the use of AI technology to enable energy sector efficiency and blockchain technology to enable P2P energy trading risks exposing household behaviors, including including periods away from home. This was a question raised by the Victorian government in a article dealing with the use of smart metersbut similar concerns would also apply here.
Artificial intelligence and blockchain technologies offer exciting opportunities to accelerate global decarbonization. As with the implementation of any technology, prior to deployment companies will need to undertake privacy and data assessments to ensure they have the necessary consents and approvals to undertake data collection and use. planned. Similarly, consumers who adopt such technologies should educate themselves about what data will be collected about them and how it may be used (including considering the potential for misuse) before choosing to adopt them. .