We are undertaking such a digital transformation within the industry and progressively within today’s energy systems for this modern intelligent energy infrastructure.
Digitalization in energy has been around for some time, improving safety, achieving greater accessibility, productivity, and understanding how we consume and manage energy within Utilities and other energy providers.
The advancement to come in what machine learning, blockchain, edge processing, artificial intelligence can all bring will take us to the cusp of a new, exciting new era of different business model understanding.
How we understand energy storage, behind the meter generation, provide different home management energy systems and position our smart grid closer to the final consumer for their home and electric vehicles needs in the future is promising shifts in demand response, flexibility and responsive networks closer to the final consumer’s future needs and demands.
The advancing technology needed for the entire energy system
Advancing technology, falling costs and connectivity promises a different architecture of this interconnected energy system.
We are only now realizing the tremendous potential for Digitalization in bringing much of the above to fruition.
To get to this transforming point, we need to break down multiple boundaries between all the different energy sectors and bring a level of integration and connectivity into an entire energy system.
This is a top-to-bottom redesign and will take years, if not decades, to complete, and in many ways, our progress on electrification will be the “beating heart” of this transformation.
What are the critical challenges to bringing about this change?
Our energy systems are inadequate for our energy needs. They have been built up over one hundred years of a patchwork design. To make such a wholesale change that will be needed to bring our energy system into the 21st Century is massive, complex and (really) expensive.
The challenges are building, not reducing complexities. Our power generation will be running in parallel for some time to come as we are undergoing the shift from a fossil fuel-driven energy system into one built on renewables and clean energy, free of carbon and methane, the principal sources of our rapidly warming planet.
The shift in power generation needs to address intermittency to provide grid stability and reliability, and interval power surges in demand and supply. The use of storage along the grid will need to be rapidly deployed to offset this power generation change, from oil, gas and coal-reliant to wind, solar, hydro and nuclear. The balancing of reserves in the system takes on a different understanding of base loads and variables.
We have inadequate and massively ageing grids. Some grids are over 50 years old and close to capacity at peak times or even worse and need massive refurbishment. To deliver a different energy type, reducing traditional losses along long-distance transmission will require a different approach in a grid design. Solutions need to address different distances, congestion (more power), various locations generating power, new ways to balance supply and demand. DERs are equally generation and storage resources (e.g., solar, batteries) connected to the grid at the distribution level “downstream” from the utility substation the type of cabling, in transmission efficiency and choice of overhead or underground. Designing new grids is highly complex to design and anticipate existing and future demand in such transforming environments.
The ability to receive, dispatch and store energy in a decentralized system, closing to the point of demand, means managing multiple power generation and storage facilities. Decentralized energy will have different asset optimization and require different forecasting designs. To learn how to juggle small distributed assets that run at parts of a day against giant plants that needs a different management philosophy.
The promising concept of managing self-consumption and managing your energy system alters the dynamics of buyer and selling, consumer and generator. The management behind the meter generation will enable industry parks, complexes like hospitals all to consider their energy independence and their potential to sell any surplus back to the grid. The influx of variable power changes loads and generation.
Competition is set to change and rise. New business models, competing fuels, utility companies losing their monopoly shift the ownership and competitive forces. Customers will seek and gain a higher level of autonomy and energy providers, both in supply, price and relationships. The principal Utility needs to focus on driving down costs in any liberalized pricing system and have multiple utility and supply strategies.
Lastly, as we redesign the energy system for today and tomorrows “fit-for-purpose”, where the complexity rises, the need for having a greater overview of the entire system requires Digitalization, distributed automation becomes the “drive towards” point. The new methods of managing complexity and the technologies to help manage this are a work-in-progress. They will need to evolve significantly in the coming years to address this.
Grappling with the Digitalization of such a complex undertaking
Connecting devices seems so easy, but it is not within an energy system that needs to stay on, deliver its existing commitments and manage the phasing out of the old and accelerating the needs to change in a volatile price/demand/supply market made up of totally different “fuel” generation.
*First the equipping of old and new sensors that turn a dumb piece of equipment into an intelligent one (think substations) is only as “intelligent” as the sensors and data you are monitoring and collecting and what each sensor can do.
*Second, the need to build robust communication networks that are reliable, dependable and resilient to transmit the data back and forth requires a radical need to think network designs and capacity, let alone the storage to house the arriving data.
*Thirdly, the software platforms to organize around, to direct and select data and take this into valuable knowledge either in real-time or over-time
*Fourth, the absolute value of analytics is only as good as the insights you can generate and the understanding by human and machine-learning understanding. Then you have to arrive at conclusions of what all of the data and insights mean and what decisions derive from this
Digitalization will need collective enabling
The need to build and increase processing power, the need to focus on the ability to achieve widespread coverage of communication networks (think remote areas), the ability to continually seek out cheaper and faster memory capacity in machines, equipment and infrastructure designs allows the growing algorithms to learn and improves (AI) and accelerate machine learning.
Here I have only scratched the surface to provide a top-level picture of the energy systems and their facing. The need to digitalize to have any chance to manage these evolving systems and stay ahead of the learning curve and still earn a profit or attract investors is daunting.
Digitalization is not “nice to have,” it is imperative to understand and deploy.
Digitalization does not just break down silo’s; it can transform our energy systems so they can cope or delay the transition we need, by our lack of appreciating the role digitalization in energy will play in future designs of the entire energy system, locally and globally.
All we have to do is convince the investor and stakeholders that they will see a return greater than their existing investments. It is hard to provide will all the complexities and moving parts within THIS energy transformation.