Since there are no currently active contests, we have switched Climate CoLab to read-only mode.
Learn more at
Skip navigation
Share via:


Rapidly deploy simple demand response technologies which do not require central approval or energy markets to create smart grid version 0.1.



Smart grid (demand response):

  • Set variable electricity prices for end users. When prices are high, conserve and use energy from storage. When prices are low, store energy.


Current Barriers to this approach:

  • Centralized action (political and bureaucratic)
  • Resistance to complicated electricity pricing schemes
  • Cost of implementation and electricity storage vs. price differentials
  • Technological development and implementation


Sidestep barriers by implementing a simpler version first.
Centralized control:

  • Avoid by using distributed protocols and heuristics. Third party could provide a rough prediction of when cutting back is most useful, or could be done by individual devices.


Resistance to complicated prices:

  • Avoid by using means other than electricity prices. Offer simple incentives (payments to users), or to reputation (for companies). Include smart control as a green feature. Or pass rules and regulations


Implement these ideas into devices and infrastructure being sold now, and make them forward compatible with potential future electricity markets.

Adopting this approach, rather than waiting for the smart grid to be implemented from above, means that technology will be developed and deployed sooner. This means that some of the benefits can start soonar, and a full smart grid system will be more effective out of the gate.

Category of the action

Reducing emissions from electric power sector.

What actions do you propose?

To achieve the goal of decarbonizing the electricity grid, the grid must be able to support a higher proportion of renewable energy sources such as wind and solar. This requires an ability to deal with the intermittent nature of energy from the sources, which varies according to the time of day, according to the daily weather, and according to yearly climate variability. One tool for achieving this is using an energy market system to price electricity according to its current availability. Prices would be low when renewable energy sources are plentiful, and high when those sources are scarce. End users would adapt to the changing energy prices by increasing consumption and storing energy when prices are high, and decreasing consumption and releasing energy back into the grid when prices are low (or rather, the end-users would set policies for a computer program to take care of the details). Sounds like a good idea.

However, there are several barriers to this vision which will at the very least delay its adoption. First, this requires centralized action by a utility or grid oversight body to set up an energy market, which might require political action, convincing bureaucrats, and a massive deployment of new technologies. Second, most of these accounts ignore the likelihood that most end-users probably don't want to deal with more complicated energy prices! Even when simpler schemes such as time of use pricing are introduced, many consumers feel slighted and anyone whose electricity bill rises will vociferously complain. Finally, it is likely that electricity prices would need to be much higher to make significant end user changes and the implementation of demand response and distributed storage an economically worthwhile option ([3] contains an analysis of energy storage in Ontario concluding that higher energy prices are required). With political will such a scarce commodity in the climate change area, increasing energy prices to support renewable energy is unlikely to happen anytime soon.

I propose to sidestep these barriers by introducing technology which does not depend on centralized control or energy pricing markets.

Centralized control can be avoided by using distributed protocols and a "best guess" approach to demand response. Although predicting generation and demand is usually the sole domain of the grid operator, in many areas it might be possible to predict a large portion of the best times for demand response using simple statistics or machine learning techniques. At a general level, it is easy to predict that the solar panels will output less on days when most of the solar plants in an area are covered by clouds, or that demand will be higher during the summer and warmer days when everyone is using their air-conditioning. It is quite possible that predictions of supply and demand could be made using a relatively small amount of publically available information, such that it could be done by entities other than the grid operator well enough to produce useful results (even if the predictions aren't always right). A private organization could do the relevant data collection and analysis, incorporating predictions from the system operator where they are available, and then come up with the best-guess as to the appropriate actions to take throughout the day. This information could then be sent over the Internet to a variety of devices able to take appropriate actions: heating and cooling systems, electric vehicles, distributed storage systems, etc. Or the information could be sent to end-users, who could decide when to take appropriate energy conservation actions: by sending the data as a push notification to a smart phone app or by providing a display on an energy intensive appliance that indicates whether or not it is currently good time to use that appliance. Or, predictions could be made by the devices themselves after downloading raw data to predict demand, or negotiating amongst themselves. This sort of technology is not too much of a stretch from currently available devices. The NEST thermostat, for example, already uses a learning approach to control home temperature [3], and there are a variety of plugs that allow you to control power to home appliances through a cell phone [4].

It should be noted at this point that this approach does have some risks. Third-party predictions of demand and best effort control of appliances using existing communication infrastructure will be less efficient than a centrally controlled system - some further research might be appropriate to figure out exactly how well the system could work. Communication protocols must be highly secure to avoid tampering.

Energy markets can be avoided by using non-cash incentives rather than market forces. An enlightened utility or government could offer flat payments or energy discounts to end-users who participate in such a system, as proposed and analyzed in [5] for a demand response system in Ontario, Canada (perhaps funded in a revenue neutral way through the reduction in expenses avoided when peak demand is reduced). Or a government could require that certain devices (such as electric cars) have demand response systems installed in them, without incurring any additional cost of themselves. If no such centralized body steps in, then participation could be encouraged by appealing to a desire to combat climate change or a desire to acquire a reputation for being green. Early adopting consumers are already willing to pay a premium on their electricity bill to support renewable energy, and many companies are willing to take action to enhance their environmental reputation already, so this is not too much of a stretch. in any case, the goal would not to be getting everyone signed up, but getting enough people to begin to influence the overall structure of demand and enough people to support development of alpha stage smart grid technologies. Since the demand response actions would not be limited to only jurisdictions in which utilities were willing to set systems of themselves, the market for these technologies will be greatly increased. This will help spur development of smart energy consuming devices and the creation of third-party standards for communication of energy relevant information. This will help speed up the adoption of the more efficient smart grid version 1.0 once centralized decision-making processes catch up. And, the existence of third-party uncontrolled demand response systems will spur utilities to implement their own demand response systems in order to have finer control, and to have enough reliability in the system to adjust forecasts of future demand and reduce capacity needed to meet peak demand.

The end result will be an early version of a widespread demand response system that can be quickly adapted to incorporate into a full smart grid once centralized decision makers are able to implement appropriate policy. In the meantime, the system can help to moderate peak demand and slightly reduce the need for fossil fuel generation within the existing grid system. It could be deployed anywhere in the world without the need for centralized approval to provide citizens and businesses an opportunity to contribute to the de-carbonization of the electricity system.

How does this proposal compare to current systems?

Examples of current systems:
Time of use pricing, as implemented in Ontario and other jurisdictions. Electricity users receive different pricing depending on the time of day, to encourage shifting of use to low demand periods. Weaknesses of this approach include: 1) centralized utility implementation, 2) reliance upon small changes in electricity pricing as an incentive, 3) does not take into account renewable energy.

Peaksaver program in Ontario, homeowners receive a programmable thermostat which the utility can change for a limited number of hours in the summer in order to reduce peak demand.Weaknesses of this approach include: 1) centralized utility implementation, 2) limited to specific technologies.

Nest rush hour rewards [6]. Nest thermostat users participating in the program can have their thermostats pre-cool their house before peak time on hot days in order to reduce demand during the peak period. Only available when the utility participates in the program, utilities can set their own reward system. Weaknesses of this approach include: 1) centralized utility implementation, 2) limited to specific technologies.

The system in this proposal differs in that it would 1) involve coordination by organizations other than utilities (which avoids delays from centralized control), 2) aim to create a standard which can be used for a variety of technologies 3) use incentives other than small changes in consumer prices.

Who will take these actions?

Businesses involved in providing demand response or making products that could be adapted to include demand response (electric vehicles, heating systems, appliances, etc.). I would envision a current electricity/smart grid player or a new company focusing on 1) demand, peak time, and renewable energy output prediction 2) protocols and software to implement demand response in a variety of platforms.

Where will these actions be taken?

How much will emissions be reduced or sequestered vs. business as usual levels?

Hard to provide an estimate, depends on how well such first-approximation systems can work and how widely they will be adopted.

What are other key benefits?

A key benefit is that the technology to support broader visions of the smart grid will already be in place by the time they become politically viable, so less infrastructure and fewer devices will need to be replaced. Showing that this simple version of the smart grid is possible might also build support for more advanced versions.

What are the proposal’s costs?

Private sector costs come from research and development of new technologies, but can be recouped by increasing sales relative to companies not taking this approach. There might be additional costs from different incentive schemes, but these will likely be regained.

Time line

Related proposals


[1] For example, see chapter 10 of Hot, Flat and Crowded by Thomas Friedman

[2] For example, see T. Carpenter, S. Singla, P. Azimzadeh, and S. Keshav. The Impact of Electricity Pricing Schemes on Storage Integration In Ontario, Proc. e-Energy, May 2012.


[4] For example, WeMo switch

[5] S. Singla, S. Keshav, Demand Response through a Temperature Setpoint Market in Ontario, Proc. IEEE SmartGridComm,  November 2012.