This blog post is discussing what is intelligent energy storage and different ways of how AI can improve energy storage solutions.
The consumption of electricity has to be perfectly matched with the generation of electricity at all times to ensure a stable and safe supply. Energy storage can help stabilize fluctuations in demand and supply by storing excess electricity and releasing it when the demand is high thus improving energy efficiency. This is particularly evident in the case of renewable energy generation such as solar and wind because of their inherent intermittency of power generation – the power is generated only when there is plenty of sun and wind but this does not always coincide with the demand in the electric grid.
Energy Storage Technologies
A variety of technologies to store electricity are developing at a fast pace and allow saving of energy in large quantities over different time periods, from fast storage in seconds to longer storage over days:
Batteries – either flow (such as lithium-ion) or solid-state batteries. Each type of battery has its advantages and drawbacks, while the price is always important for investors
Flywheels – An electric motor is used to spin up a wheel or rotor to store energy, then energy is discharged by an electric generator, thus spinning down the flywheel.
Compressed Air Energy Storage – utilizing compressed air to create a potent energy reserve
Thermal – capturing heat and cold to create energy on demand. Storage media include water or ice-slush tanks, masses of native earth or bedrock accessed with heat exchangers by means of boreholes, deep aquifers contained between impermeable strata; shallow, lined pits filled with gravel and water and insulated at the top, as well as eutectic solutions and phase-change materials (latent heat storage (LHS) units).
Pumped Hydro-Power – the water is pumped from a lower elevation reservoir to a higher elevation during low-cost surplus off-peak electric power. During periods of high electrical demand, the stored water is released through turbines to produce electric power. Although the losses of the pumping process make the plant a net consumer of energy overall, the system increases revenue by selling more electricity during periods of peak demand, when electricity prices are highest.
What Is Intelligent Energy Storage?
What all of these technologies have in common is that although they are highly advanced pieces of engineering, they are by themselves still just “dumb” machines that store and release energy but they can’t:
Optimize their operation by controlling when they are charged and discharged depending on electricity price,
Control how and when they are charged day-to-day to achieve longer cycle life,
Coordinate their operation with other energy storage solutions, power generation capacities, and consumers,
Predict when a failure will occur and act before it happens. For example, if a lithium-ion storage system suffers a thermal runaway or other degradation-inducing events, the batteries will not recover, and the system may become a permanently low-performing or even stranded asset.
Where Artificial Intelligence Comes Into Play
Artificial intelligence (AI) is an approach to computing that uses large amounts of data in order to accomplish a task. AI is particularly powerful where large volumes of data exist that can be harnessed to “teach” computers to “think and act as a human”. Energy is one such field where a lot of data exists, is readily available, is well structured and accurate and is particularly suitable for various kinds of AI solutions. We can use this data to feed it into an AI system to deliver results that can hugely benefit energy storage solutions.
Here are a few examples of AI improving energy storage solutions:
Demand and generation forecasting: AI is particularly good at forecasting electricity generation and demand and consequently the price at a particular point in time. It does so by taking into account years of historical electricity data, weather data (particularly hot or cold days require more air conditioning or heating, this driving the electricity demand) and other data.
Accurate forecasts not only support the safe and reliable operation of the grid (by balancing electricity supply and demand), they can substantially improve the operational efficiency of energy storage solutions. If one knows in advance what the price of electricity will be at each hour during the day, then one can store electricity when it is cheapest and release it during the peak hour and thus maximize his assets.
Wind and solar forecasts: are critical to reducing the uncertainty associated with variable renewable energy generation; scheduled delivery of energy output is valuable to the grid than standard, non-time-based delivery. Making use of that energy can be difficult because knowing how much a given farm will generate and how best to store and then deliver that energy to the grid changes every day.
To develop wind and solar generation forecasts, AI utilizes a combination of weather and satellite data, numerical weather prediction models, and statistical analysis to produce estimates. A lot of energy storage solutions are deployed to balance the uncertainty of wind and solar so wind/solar forecasts are crucial for these systems.
Predictive maintenance: AI can detect anomalies across a range of electrical, electro-mechanical, chemical and thermal subsystems before they cause any damage to the system, enabling the operator to act in time. AI does so by gathering data from different sensors and the environment and comparing it to historical data that was used to teach AI what set of conditions typically lead to a failure of a certain component. Using predictive maintenance can reduce downtime, improve the operational life of a storage system, prevent damage and increase profits for the operator.
Intelligent building control: AI can be used for super-detailed modeling of energy use across buildings, including detailed predictions of passive solar capacity, wind speed, and building energy load. This would allow building energy storage to be used in the most optimal way and reduce building energy consumption.
AI is already shaping smart grids to become truly smart and enables them to meet the energy demands of the future. Energy storage is an ever more important part of the smart grid and AI will revolutionize our understanding of consumption patterns and tweaking the operation of these devices to drive the revenue for its operators.