What Is the Smart Grid?

Decentralized generation, along with other technological innovations such as smart meters and batteries, all increase the fragmentation of the electricity system and therefore the need for and the cost of coordination. This is where the so-called “smart grid” comes into play. The smart grid is an answer to the coordination needs of the grid operator as created by unbundling and decentralized generation. The crucial question is whether the smart grid will be able to coordinate itself and thus substitute itself to the system operator; in other words, whether a digital platform connecting buyers and suppliers, including all the necessary information about the status of the grid will, in the long run, be able to substitute itself to the system coordinator.

To be clear, the “smart grid” is not about the grid; rather, it is about information management in an increasingly complex and fragmented electricity system. As such, it relies on the above presented smart meters; that is, on devices capable of measuring and interrupting electricity flows throughout the grid, mostly at the distribution level. More appropriately, we should talk about a “decentralized smart grid.” In short, the smart grid is essentially a data-processing mechanism capable of integrating the information available throughout the grid, whether at its generation, transmission, or consumption levels. The information so collected is then essentially used for managing the demand of electricity at the decentralized level.

For example, industrial and domestic air conditioners, refrigerators, and heaters can be made to adapt their activation cycles to avoid activation when the grid is suffering a peak condition. There are now many examples of smart grid applications, whereby a certain number of homes, and in particular the appliances within these homes, using smart meters, are being connected via the internet and managed by a coordinating digital platform to make efficient use of both the consumed and (decentrally) generated electricity. However, all of these smart grid operations remain experimental and, as of today, cannot be scaled to larger regional and national levels.

Managing the data as collected through all these smart meters has numerous advantages. For example, smart meters can, to a certain extent, automatize such management by detecting errors automatically, interfering into the grid by stopping certain consumption or generation units, or even by helping repair certain things or routing electricity flows elsewhere. All this increases the resilience and reliability of the grid. It also helps reduce its vulnerability. In that sense, the smart grid - that is, a digital platform coordinating supply and demand as well as the status and the operations of the grid - can to some extent substitute or platform the decentralized coordinator, which, in this case, is the DSO. As explained above, it is the DSO that delivers electricity to the final household and must accept the electricity that is being generated at the decentralized point, such as from the solar panels on a rooftop. It is also the DSO that is directly affected if households selfconsume (“presume”) and thus buy less.

Furthermore, the smart grid can better handle bi-directional electricity flows; in other words, it can manage decentralized generation much better. As such, it is actually a corollary, if not a condition for decentralized electricity generation. A smart grid can better allocate these flows and make the grid more flexible and more adapted to decentralized generation.

Probably the most important contribution of the smart grid lies in the efficiency gains for the grid operator, but also for the consumers who become also producers, the so-called prosumers. Better management of electricity flows leads to lower needs for investment and ultimately also to lower grid tariffs. This is like MaaS in the case of transport: smart primarily means “more efficient.” Of course, these efficiency gains are offset by the cost of the smart meters and the management of the data, including the energy costs of managing and storing all these data.

Better management of electricity flows also leads to better load balancing and lower load balancing costs, at least at the decentralized level. Thanks to all this information, consumption patterns can be much better predicted using algorithms. Peak demand can be reduced and load curves can be much better balanced; this is also called peak-curtailing or peak-levelling. This again is both beneficial for the decentralized grid operators (DSOs) and for the consumers, but probably least to generators and retailers who are in the business of selling as many kilowatt hours as possible. Even they can take advantage of the smart grid, as they can better plan their generation capacities according to consumer demand, grid availability, and grid costs.

There may even be new business opportunities, both for generators and grid operators inasmuch as they can offer, thanks to all their data analysis, consulting services to customers to optimize their consumption profiles and demand response mechanisms. Similar consulting services can also be offered to government and policy makers. Corresponding information platforms can be a first step towards more sophisticated energy platforms.

 
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