Agents in the Smart Grid

Readings on smart grid futures

Archive for February 2011

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

On Demand-Side Management

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Since power systems specialists tend to be very good at controlling the grid and the suppliers on the grid (either via energy markets or through regulation), the consumer-side of the problem has always been quite elusive. The term ‘demand-side management’ (DSM)   aims to capture all the possible mechanisms by which demand can be contolled (effectively, ‘management’ seems to be the non technical term for the more controversial ‘control’ term that could have been used).  This may be an over-generalisation given that the ways in which demand can be controlled can be further broken down into a number of categories:

  1. DSM via pricing mechanisms – to incentivise consumers simply based on costs to reduce peak demand or shift consumption to later times.
  2. DSM via decentralised coordination mechanisms – the actors involved could be exchanging messages in order to agree to curtail demand (as in a Virtual Power Plant) or to provide energy back to the grid at times where it is needed (e.g., electric vehicles coalescing to provide energy back to the grid).
  3. DSM via centralised control of generation/consumption – the most drastic form of DSM.

The above categorisation shows that DSM, as it will be carried out in the smart grid, will have to take into accound that consumers will not just be consumers and would also be producers, hence the term prosumers. The term prosumer was coined by futurologist Alvin Toffler [1]. Toffler defined the prosumer as someone who blurs the distinction between a “consumer” and a “producer.”  In many other domains, a prosumer means various things (e.g., someone who works at home and at the work place [2] or even a professional consumer –see wikipedia entry ). However, in the energy domain, and in the smart grid in particular, prosumers will become complex first-class objects of their own. These prosumers are characterised by their:

  • Demand elasticity – how they respond to prices based on how they view their need for energy but also how they can shift consumption across any given time period by virtue of their energy storage capacity or the deferrability of their devices with respect to their impact on their comfort.
  • Supply capacity and availability – their micro-generation capability may widely vary – starting with individuals generating energy from their solar panels which are affected by the weather to VPPs of facebook friends banding together to sell energy from their combined CHP/solar panel installations. Note that supply = reduction in demand and hence supply capacity/availability is equivalent to how these actors reduce their demand according to their storage/deferrability of their loads.
  • Social context – as highlighted in the previous point the actors may wish to band together to form VPPs or even decide to run energy cooperatives/consumer groups keen on finding ways to reduce their consumption proactively or sell back to the grid their extra generation (curtailment) capacity. However, the conscious embedding of energy-related activities into the traditional social activities of consumer will require a significant paradigm shift. Similar to the shift caused by mobile phones which allows people nowadays to maintain their social relationships all the time (e.g., checking-in using Facebook places and FourSquare), electric vehicles or smart meters could ideally, one day, allow people to all coordinate to shift their energy and literally ‘check-in’ into some demand-reduction exercise in real-time. However, while the motivation to ‘check-in’ and form groups is natural when it comes to sharing interests in movies and books, it is not so much the case when it comes to energy. The benefit of switching off lights, turning down the thermostat by 2 degrees sounds greatly appealing on the first day you hear it but is quickly forgotten later on. Something (possibly an intelligent agent) has to keep this up.

The prosumer is a complex animal not yet fully understood and likely to morph into something most people (engineers in particular) are scared about. Building more predictability into the system is of immense importance. Some of the main reasons are as follows:

  • If prosumers decide to all charge their batteries or defer their loads at times when prices are low, they can and will cause higher peaks than currently exist. This is one of the main worries expressed in the power systems community [3]. Indeed, the grid makes significant savings in terms of generation capacity needed given that the energy consumption of typical consumers is so diverse – that is, they tend to consume their absolute maximum at different times of the day. This means that, when their demand is aggregated, it is never the case that the total demand at any point in time is equal to the sum of everyone’s peak demand! DSM may break this natural diversity.  To illustrate this see the figure below which shows the peaks that would have been generated in the grid if consumers were simply reacting to prices (i.e., choosing to consume at specific times of the day given that they could defer their usage of electricity at any time irrespective of the cost of comfort incurred) on the grid for example [4]. One of the key ways such peaks may be reduced is to engineer inertia into the shifting of loads using machine learning techniques as we showed in [4].

Figure showing the consequences of applying Time-of-use pricing, Spot-pricing (day-ahead prices), and a flat price (as it is currently).

  • If prosumers decide to buy new batteries and new CHP units/solar panels  in the home, their consumption/supply capacity and availability may change significantly throughout the day (depending on how users decide to use energy) and throughout the year (as new and old devices are used, fail, or serviced). Hence, the traditional mechanisms used to make sure supply capacity is available days, weeks, or months ahead may not apply anymore. The environment will be much more dynamic. One of the key ways this may be managed is to ensure that there is enough redundancy in the system, that is, making sure that the supply from multiple providers is contracted and used if needed.
  • Social activities will dictate how prosumers create peaks or contribute energy (or reduce energy consumption) to the grid if prosumers are given enough incentives to do so. Incentives, however, cannot simply come in the form of prices as, not all social activities can be matched to cost savings. For example, having dinner in a nearby restaurant instead of the sushi bar you prefer 3 miles away, may not be the action you decide to take in case going to the nearby restaurant will save you 5 dollars and help the grid reduce carbon emissions. People like to be together and will do anything to do so (e.g., fans of Justin Bieber travelling to see him in London from all corners of the UK). At this point in time, there is no obvious solution to this. The socio-technical challenge involved is immense.

The use of intelligent agents in managing devices, storage, and generation capacity is a foregone conclusion. Several major challenges, however, still lie ahead in terms of modelling energy usage optimisation problems and addressing the key human-computer interaction issues involved.

UPDATE: I saw this article on the Prosumer in the smart grid today! :

[1] A. Toffler, W. Longul and H. Forbes (1981) “The third wave”, Bantam Books New York.

[2] William Gerhardt (2008) “Prosumers: A New Growth Opportunity”, CISCO.

[3] G. Strbac (2008) “Demand side management: Benefits and challenges“, Energy Policy, Volume 36, Issue 12, December 2008, Pages 4419-4426, Foresight Sustainable Energy Management and the Built Environment Project.

[4] Ramchurn, S., Vytelingum, P., Rogers, A. and Jennings, N. (2011) Agent-Based Control for Decentralised Demand Side Management in the Smart Grid. In: The Tenth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2011), 2-6 May 2011, Taipei, Taiwan. (In Press)

Written by agentsinthesmartgrid

February 19, 2011 at 11:27 am

On the deployment of Virtual Power Plants

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A Virtual Power Plant (VPP) is a concept derived from the ‘Virtual Utility’ metaphor used by Awerbuch and Preston [1] in the late 1990’s to describe the aggregation of distributed energy generators and consumers typically located in the distribution network of the grid. The key challenges identified by [2,3] are as follows (with my comments):

  • A clear definition of the key characteristics of the control interface exported by distributed energy resources (DER), which could be generators or controllable loads. How could such interfaces lead to portfolios of DERs be defined for the overarching CVPPs (Commercial VPPs) and TVPPs (Technical VPPs)?

    A possible answer to this would probably be to produce a clear ontology and a language to express the capabilities of the DERs and their feasible connections and conflicts. Does an ontology already exist to express DER capabilities and controllability? What would be the best formalism to express the feasible associations/conflicts among the DERs? Representations of plans (e.g., TAEMS) for multi-agent teamwork come to mind as these have been used to express such issues in very dynamic and uncertain settings (military – the COORDINATORS programme)
    as in the smart grid.
  • Distribution energy management – i.e., how to make sure the energy produced or used in the distribution network is properly managed to avoid overloading lines and keep voltage levels stable.

    The authors in [2] show, using optimal power flow equations, how the output from a VPP can be significantly affected by the conditions on the network. Power ouput may have to be severely curtailed by the distribution system operator (DSO) who would be mimicking the role of a TSO (transmission network operator) as it is on the tranmission network at the moment. Wouldn’t this unravel as the number of DERs scales to hundreds of thousands as consumers start using smart meters and solar panels in their home? The challenge would be to design distributed control mechanisms to ensure lines are not going to be overloaded as a result of (un)-coordinated actions (production/consumptions) by individuals.

  • Providing a commercial and regulatory framework – i.e., how to ensure the energy produced/consumed is paid for/billed for exactly.
    Aggregating resources technically is a different challenge from the commercial aggregation given that each individual resource is potentially owned by a different company which will try to maximise its profits. It seems, so far, that each DER will only get paid for what it produces at the end of the day, no matter what the others in the VPP produce. However, this is not entirely sane given that each DER is serving to compensate for others’ inability to produce at certain times as pointed at in [3]. Given the increasing heterogeneity of DERs, it can be expected that the negotiation power of some of these bigger/more reliable DERs will push others to share parts of their earnings with them. This points to the need to devise payoff sharing mechanisms that users are comfortable with. Cooperative game theory points to different ways of doing this through solution concepts such as the Core/Kernel/Shapley value but these tend to be intractable or rely on some simplifying assumptions about the actors’ utility functions. In the smart grid, solution concepts that are computable in real-time will be needed.

  • Providing the communication platform for all the components of the VPP to talk to each other.
    Since the VPP is likely to be composed of lots of individual actors it is important to design the communication infrastructure such that it is robust to failures but also to delays in comms. Conversely, the decision making algorithms of individual components would need to be able to manage any latency or failure in the communication infrastructure in order to guarantee stability.


[1] Awerbuch, S. and Preston, A.M., (1997) “The virtual utility: Accounting, technology \& competitive aspects of the emerging industry”, Kluwer Academic Pub.

[2] Pudjianto, D.; Ramsay, C.; Strbac, G.; , “Virtual power plant and system integration of distributed energy resources,” Renewable Power Generation, IET , vol.1, no.1, pp.10-16, March 2007 , URL:

[3] Dimeas, A.L.; Hatziargyriou, N.D.; , “Agent based control of Virtual Power Plants,” Intelligent Systems Applications to Power Systems, 2007. ISAP 2007. International Conference on , vol., no., pp.1-6, 5-8 Nov. 2007 URL:

Written by agentsinthesmartgrid

February 15, 2011 at 10:25 am

Posted in microgrids