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Intelligent Energy Storage for Grid Modernization Strategies in a fast-growing complexity

image credit: Credit: Hybrid Energy Storage and AI | Smart Phases Inc.

Stephane Bilodeau's picture
Chairman and Chief Technology Officer Novacab Inc.

Dr Stephane Bilodeau, Eng., FEC has a PhD in Energy & Advanced Thermodynamics as well as a Master in Applied Sciences. He is a Fellow of Engineers Canada. In the last 20 years, he has driven...

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Increasing complexity

With all that we hear about Artificial Intelligence, you can imagine what would be the impact of an intelligent tool in the energy field. The integration of intelligent energy storage (IES) in the mix is even earlier in the deployment. Concretely the challenges of the “really-intelligent” integration of Smart Grids and Energy Storage is to answer the emergence of the new needs of the consumers and the producers of energy, accompanied by the mass generation of data on their state and necessary to their operation. The direct impact is the sharp increase in the complexity of energy networks. This increase in complexity is great news! It tells us that we are in the process of performing or materializing our energy transition.

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Credit: Intelligent Power Generation Control (CC BY-NC-SA 2.0), © Smart Phases Inc. (DBA Novacab) Kick the (AI) tire!
Credit: Intelligent Power Generation Control (CC BY-NC-SA 2.0), © Smart Phases Inc. (DBA Novacab

It’s really started, but not yet achieved. Several ruptures result of this transition. Take the case of the power grid on which I’ll focus on in this article, knowing that it is applicable to all other types of Smart Grids mentioned above. 

The first disruption is the transition from a hierarchical and asymmetric grid to a set of distributed and dynamic grids. We move from the model where the power plants produced energy that was distributed to consumers to the model where everyone can produce, consume, sell, buy or even self-consume on their own or within a community. In short, the new network model is a dynamic and distributed model where each can be an individual, a municipality, a community of individuals or municipalities, an industry or a central.

The second rupture is the shift from near constant production with little variation to largely intermittent and fatal production related to renewable energy (ENR) and the necessary storage of energy.

The third is the partial loss of control over the grid by the grid operators due to the emergence of new uses with their own balancing logic: electric vehicles, self-consumption community, etc. It this all needs to be done, while still requiring peak shaving, load shifting, valley filling, conservation at the same time as the total load is still growing. This results in the emergence of significant complexity of flows and islanding of grid structures that paradoxically reinforces the need to balance the global network.

Peak shaving, load shifting, valley filling, conservation while the total load is growing
Peak shaving, load shifting, valley filling, conservation while the total load is growing

A genius tool

Some are using a simple way to define Artificial Intelligence is “software that can learn and decide in a relevant way”. Others would say that it is a “Set of theories and techniques developing complex computer programs capable of simulating certain traits of human intelligence (reasoning, learning …).” The World Economic Forum, which took place last January in Davos, discussed different topics (ref. EnergyVoice) including the usefulness of artificial intelligence in the energy transition in true purpose-driven tech development. It was explained that AI can facilitate the eco-design of products better integrate Renewable in the energy mix, simplify the development of new business models, which will be designed to be more energy efficient and notably to manage (and reduce) waste of all sorts in a large scale. Artificial intelligence could allow for rapid development of an achievable circular economy conducive to the energy transition, and there is urgency!

Credit: Realizing the Energy Transition (CC BY-NC-SA 2.0), © World Economic Forum / Ciaran McCrickard
Credit: Realizing the Energy Transition (CC BY-NC-SA 2.0), © World Economic Forum / Ciaran McCrickard

But also, it can help through the biggest challenges and make sure that scale-up is achievable. The complex network of technology needed to usher the transition to low carbon sources needs to be capable of scaling-up to align with global needs. One of the most prominent examples of this is large-scale and long-term energy storage. Concretely, the AI would quickly calculate many data, before associating them, thus making them usable.

It is now clear to just about everyone that AI is not a simple topic in itself but rather a genius tool for transformation affecting every imaginable area. Optimally integrate Energy Storage with AI (the IES or Intelligent Energy Storage) to efficiently perform Energy transition with clean energy is a natural pathway forward. That will “disrupt” the conventional ways, but this combination has the potential to solve the biggest of the (exponentially growing) challenges. One way to explain this exponential capacity of the combo tool is that AI algorithms are extremely effective at handling highly complex and non-linear situations as the world becomes more complex each day. The energy transition can not do well and fast without optimizations gained from the IES, the return is no longer an option! In a “Combo Effect”, IES can also help to meet certain shortages in remote areas and thus reduce territorial inequalities.

IES is, therefore, the new solution, to reduce our impact on the environment, while integrating perfectly the sustainable sector into the global economy.

Fast and reliable control of energy consumption and production

Artificial intelligence can, therefore, adapt to many if not all sectors of the power industry. But how is this going? When used in production or energy consumption, AI can work through sensors, which are installed in control systems. These sensors make it possible to analyze any data, for example, temperature, vibrations or the power of the flows.

This allows for real-time processing of the data. Thanks to this, anomalies or malfunctions of the system are detected and supported much more quickly. Once the problems are highlighted, the defective system or equipment can be replaced, this maximizes the energy efficiency while avoiding waste (example: leaks on a steam line or intermittently failing solar panels or storage modules).

Soon, artificial intelligence will even be able to detect weak signals, analyze the obsolescence of a component and recommend it in the wake! The maintenance of the system will be optimized and the production never interrupted.

In short, artificial intelligence represents a real solution to better consider the energy transition. Its major advantage: it is available to different industries. In addition, the ideas fuse to push it and make it always more optimal.

IES for Grid Modernization Strategies

IES can help build multi-year, transformation grid modernization programs for operators or regulators. The focus is on capability building, from enhanced situational awareness to the deployment of intelligent infrastructure. IES ensure that the integration of bulk and distributed renewables and becoming the grid of the future for customers are central tenets of future strategies. It is a complex Integrated program including Analytics, Customer Empowerment, DER and Bulk Renewable management, Integrated Systems, Network Automation and Physical and Cyber Security. So, for Smart Grid Deployments, IES should be there from the start. It means to explore the connected customers’ market, what a future of transactive energy may look like and whether innovative regulation like Reforming the Energy Vision in New York or California’s AB 2868 will have impacts across other jurisdictions.

Through rigorous assumption, sensitivity and scenario analysis AI develop business cases for utilities to ensure that investments in new technologies are optimized and can be accelerated and that energy storage is well integrated for Renewables project are built for the future.

Credit: Optimizing power generation with Hybrid Energy Storage and AI (CC BY-NC-SA 2.0), © Smart Phases Inc. (DBA Novacab),
Credit: Optimizing power generation with Hybrid Energy Storage and AI (CC BY-NC-SA 2.0),
© Smart Phases Inc. (DBA Novacab),

In 2018, Cédric Villani, member of the French Parliament and renowned mathematician, presented a parliamentary report, soberly titled Giving a meaning to artificial intelligence. Surrounded by a team of seven specialists, he questioned the French and European strategy in artificial intelligence:

“Like many mathematicians starting their career in the 1990s, I deeply underestimated the impact of artificial intelligence, which finally gave, at that time, few results,… What a surprise it was to witness, in the 2010s, the incredible improvement of its performances. […] Moreover, in recent years, no one can escape this subject polymorphous, so he has become ubiquitous in economic and social discussions,” says Cédric Villani in his Forword.
Credit: Photo by Steve Halama on Unsplash

In the case of the priority objective of global reduction of the carbon footprint, the report proposes to concentrate the AI ​​efforts in two sectors: the optimization of the modes of transport and the transition towards more rational and less polluting agriculture.

How? Mr Villany even proposed a sectoral policy, which would have a non-negligible mass effect, and would constitute a major step forward in the energy transition. All this without forgetting, in parallel, to systematize the recycling of the heat produced by these data centers that receive these exceptional tools! Because with the artificial intelligence of tomorrow, nothing must be lost anymore.

The bright side is that artificial intelligence and energy storage also carry with them a panoply of solutions in terms of ecological and energy transition. The 2018 MP Villany’s report added:

“AI opens up radically new perspectives for understanding and preserving the environment. Whether in terms of identifying and preserving biodiversity, repairing the damage, modelling the impact of our actions, optimizing the use of resources, developing renewable energies, AI can help reduce all our consumption and amplify all our actions.”

This is good news, but we can’t wait for policy to start working on the integration of these innovative technologies into our grids, big and small. This will also greatly help to quickly improve the resiliency of the grids in front of the increasing (in strength and occurrence) storms, fires, droughts, floods and other impacts of climate change; improving not only CO2 emissions but also energy efficiency, independence and responsiveness, especially in remote communities. The energy transition is required as soon as possible. It is now “furiously” time that we build the future.

So, warm up your biological neurons!

There might be a “Fast and furious” Energy Transition ahead!

Photo by toine Garnier on Unsplash


This article is part of a series on Artificial Intelligence and Energy Storage by Stephane Bilodeau, ing., P.Eng, PhD, FEC, is an entrepreneur in cleantech, working and teaming on innovation, notably in energy storage, renewables, and artificial intelligence. He is the Founder and Chief Technology Officer, of Smart Phases (Novacab), Fellow of Engineers Canadaand expert contributor to Energy Central and to Medium.

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