Agents in the Smart Grid

Readings on smart grid futures

Data in the Smart Grid

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This is just a short post in response to this article in the IEEE Smart grid newsletter on ‘Slicing and Dicing Smart Grid Data’,  by Christos Polyzois which states that:

If a utility tried to collect detailed consumption data at a central site in real-time (interactively) once every 15 minutes (let alone more frequently), each customer would generate almost 100 transactions per day. This number is a multiple of the number of banking, credit card and airline transactions that a household generates per day, which implies that the smart grid would necessitate an IT infrastructure (computing and communications) larger than that of the banking, credit card, and airline industries together.

The author calls for the different types of data to be identified and managed differently:

Broadcast data. To balance the grid, data concerning grid status need to be communicated to market participants. That includes information on price changes, critical peak events, reliability signals, and–in the future-carbon content. Availability of this data makes the electricity market efficient by allowing consumers to make informed decisions, leading to better allocation and utilization of resources.

Comment: Some of this data is already broadcast over the internet in the UK at least – however tools are not readily available to filter down this data to users. AlertMe and GridCarbon are two apps that at least show the users how they fare against others near them and how much carbon the grid generates in real-time respectively. However, it is unclear what the benefits of broadcasting the data are – more user evaluations are needed to determine the impact of this information. Of course, if an agent (managing a storage device or a micro-generation facility) obtains such signals, it is likely to ‘react’ to them without consideration for what other agents ‘reacting’ to the same signals would do. In fact, if all agents react to a low price at the same time (as we’ve shown in our work – see my previous post), significant peaks could be generated in the system and hence, such signals could be detrimental to the system. Hence, the reaction to such signals need to be well studied before assuming that ‘better allocation and utilisation’ of resources will be achieved.

Billing interval data. For price signals to make markets efficient, some form of variable pricing must be in effect (time of use, critical peak pricing, peak time rebates, and so on). A utility needs to take readings at the beginning and end of a period during which a particular price is in effect, so that the utility can bill its customers properly.

The idea of billing a user for its periodic consumption aims to filter down the costs in the balancing market to the consumers. At the moment they are agnostic to such costs because of flat price/TOU pricing profiles. BUT will the users actually react to more accurate billing – will they care about a difference of 5 pence for half an hour? Again, on the utility’s side, accurate billing is good but it needs to be accompanied by consumer education or feedback (through the smart meter) to be of any long term value. It is to the advantage of the utility to do so as, making consumption flatter, will help it buy more energy in bulk in the forward market and possibly trade in futures as well.

Detailed consumption data. Energy customers need to know their current rate of electric consumption so that they can adjust it; they may also use automation controls to set their preferences so as to avoid having to constantly take actions.

Again, here the issue of human behaviour come up. It is not clear from existing results that users will indeed react to real-time prices or  want to automate the control of devices – they’d prefer to defer them themselves and might not let this deferment to be more than a certain number of hours (Note: I’ll add some references on this later).

Aggregate statistical data. Energy service providers may show customers their consumption by month, give them comparisons with neighbors or with historical time series, and other analytic results based on historic usage.

The impact of such data will probably help make users aware of their energy consumption and it is important to study the impact that the social network of a user can have on her energy consumption. Google PowerMeter and AlertMe try to do the same but, so far, no serious evaluation has been done in this space.

Written by agentsinthesmartgrid

February 22, 2011 at 10:09 pm

Posted in Data

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