The Zero Marginal Cost Future: Energy as Data
By Vangelis Andrikopoulos and Lawrence Lundy-Bryan
Our energy system has failed. Ever since the industrial revolution our economy has mainly been powered by non-renewable sources of energy such as oil and gas. CO2 emissions are harming the environment while the global energy demand is expected to rise by 48% by 2040. By 2050, 68% of the world population will be living in cities driving electric vehicles. New energy sources such as solar panels and wind turbines are proliferating and combined with improved batteries are enabling energy to be generated in a distributed fashion. We are increasingly adding geographically distributed mini-power plants, producing smaller amounts of energy that feed into newer local micro-grids and traditional large grids.
Our current energy systems are unsuitable for our changing energy needs. We need a more sustainable, secure, and efficient energy ecosystem that enables us to collect data, keep track of it and generate insights that help us and the system itself improve. Let us explore how internet of things, artificial intelligence and blockchains converge and shape the industry.
Internet of Things capture data and digitize the grid
The energy infrastructure, as a consequence of its reliance on oil and gas, has struggled to adapt to changing consumer demand. Once electricity passes into our houses, for example, we lose track of it. As fossil fuels are slowly phased out, variable renewable energy sources are “plugging” into the grid. These sources are unpredictable due to their dependence on weather and make load balancing more challenging. We cannot monitor, control, and improve what we cannot measure.
IoT sensors and smart meters turn any device into a smart one. Firstly, they allow us to collect consumption data from appliances such as fridges, ovens, lights and thermostats, as well as electric vehicles. Secondly, we can monitor energy production of any distributed renewable energy source such as solar panels, wind turbines including any devices that are equipped with batteries. For example, electric vehicles, or storage facilities can consume but also store energy and feed it back to the grid. Thirdly, data from energy distribution and transmission helps us minimize energy loss and balance the load more efficiently. Xage is using blockchain technology to create a security fabric and secure IoT devices. The more IoT devices connect to the network, the safer it becomes.
Blockchains track and secure data
Once we gather data from IoT we then need to secure, keep track, and distribute it across networks safely. Currently it is challenging to ensure that a device plugged into the grid has not been tampered with and validate its digital identity. As an increasing number of renewable sources and devices equipped with batteries interact in energy networks, the process of energy trading and certifying renewable energy production becomes more challenging. Cyber attacks on energy systems have exposed vulnerabilities that threaten national and personal security.
Distributed ledgers use cryptography to safely timestamp data and transactions. They also provide a mechanism for the transaction, verification, and storage of digital assets such as renewable energy certificates and power purchase agreements. WePower is using blockchain to connect businesses with renewable energy generators and securely facilitate direct transactions. To allow all devices exchange energy in a secure and automated way, Energy Web Foundation is using proof of authority. Network must agree on a change of the ledger in a way that is not reliant on a single entity or administrator. Such changes could be simple transactions, or smart contract functions being triggered. This could be when a certain amount of renewable energy has been harnessed to form a renewable energy certificate or an autonomous vehicle automatically pays to purchase energy from a local grid.
Learning algorithms optimize energy production, distribution and consumption
We have now time-stamped and secured our data. Next step is to analyze the data in order to improve load balancing, optimize production and save energy. DeepMind, in their recent wind project, is using learning algorithms to increase wind turbines energy production. The result was a boost of approximately 20%. Storage facilities and devices equipped with batteries can benefit from machine learning too. They can monitor the balance in real-time in order to ‘decide’ when to buy and store or sell energy depending on the grids’ needs. Equally smart lights can dim their brightness accordingly based on the presence of humans and vehicles to save energy. Machine learning has also proven to be very effective flagging up abnormal network activity and exposing new vulnerabilities.
As with other sectors such as financial markets, humans are not the only participants in the energy sector. The convergence of IoT, blockchains and artificial intelligence enable every ‘thing’ to be a smart, autonomous economic agent and act in the best interest of their owner. For example, an electric vehicle buy energy when demand is low at a low price and sell it back to the grid when demand rises for a premium. Your home storage facility could automatically trade energy too and make money while you sleep. Our energy system has failed and data is more important than the energy itself. The Convergence Stack is a valuable guide to build the next era of energy.
