The Intelligence in Using Renewable Energy
While it is hard to ascertain by when, it is widely acknowledged that fossil fuels are not the future sources to fulfill our energy needs. Renewable energy is emerging as the most reliable alternative for our future, however, despite significant progress in pockets, there are quite a few daunting challenges. Among the leading sources of renewables, solar and wind are dependent on the weather, are unpredictable, and we still need to smoothen the flow of energy from generation to consumption. The advancement in storage technology is promising yet far from where it needs to be. From time to time, generation of renewable energy will fall short of demand and hence it cannot be the reliable source of base load for consumption. The base load continues to shift to fossil fuel-based generation which defeats the purpose to move to renewables in the first place.
One solution is to use demand-side flexibility cleverly, cutting demand for renewable energy when supply is low and bumping it up in times of plenty. This is possible by advanced load control at an equipment and appliance level, such as large air-conditioning units or industrial furnaces to switch off when power generation is low and consume more energy when there is excess supply. Additionally, the owners of this equipment could also be contracted to make their stored energy and battery packs available to the grid when required.
While the concept is sound, there are a few problems when it comes to implementation. Before the grid can decide whom to tie up with and what tariff to pay, it must know the number of devices in play and the extent to which they will participate. It also needs to safeguard the energy consumption data it will collect from those devices from being misused and misinterpreted.
My view, deployment of Artificial Intelligence (AI) and machine learning technologies can resolve most of these issues. By applying machine learning to the data generated by advanced sensors, smart meters and intelligent devices beyond-the-meters, grid operators can estimate how individual appliances behave. They can also use algorithms to predict the storage life and accordingly determine the payouts to be made.
In the case of large Commercial and Industrial consumers like supermarkets, office buildings, factories, railways, grid operators can use AI to analyze relevant operational data from all the relevant equipment, such as solar panels and cooling systems, to make informed real-time decisions to maximize demand flexibility. Germany, for instance, is using a machine learning-based early warning system that takes real-time data from wind turbines and solar panels around the country to predict the energy that will be generated over the next two days.
Other ways in which AI can facilitate demand flexibility is by employing game theory algorithms to devise incentives to improve overall participation, and leveraging blockchain or other distributed ledger technologies to protect data. It is possible to create a market place for consumers to participate in demand side management initiative in their local market.
While managing intermittency of renewable energy is the biggest goal, AI can also help the industry improve safety, reliability and efficiency. It can also provide visibility into energy leakage, consumption patterns and equipment health. For instance, predictive analytics can take sensor data from a wind turbine to monitor wear and tear, and predict with a high degree of accuracy when it would need maintenance.
Artificial Intelligence technologies can also help renewable energy suppliers launch new service models and expand the market place for higher participation. By applying AI to data pertaining to the energy collected, the industry can gather granular consumption insights that it can use to introduce new service, the industry can also locate upstream/ downstream products operating in dynamic pricing models. This will also create an opportunity for retail suppliers to tap into the consumer market.
And perhaps best of all, AI can also facilitate the development of strategy, policy, and planning, around current use and future demands. I can’t help but think, that can only mean one thing - more power to human potential!