Policy Focus: Energy
Status of Implementation: Appraisal
EU Climate Relevance: Non-ETSRED


Growing energy consumption and variability of energy demand profiles since the late 20th century have increased capacity requirements for power plants  to satisfy usage demand around peak times. Some of this generation capacity lies idle during off-peak hours generating significant economic and environmental costs. A potential method for addressing this involves  demand side management whereby consumption is redistributed throughout the day (Avancini et al., 2019). Smart meters can facilitate various demand side measures using digital communication technology to provide frequent, accurate energy usage information to consumers and electricity suppliers.  These measures range from dynamic pricing schemes to comprehensive two-way digital communication between the home energy system, home appliances and the broader electricity grid. Connecting homes to the “smart grid” with smart meters can facilitate the automation of appliances to optimize home energy consumption and the integration of local microgeneration and renewable energy sources into the electrical grid. These measures are central to the transition toward a flexible, low-carbon energy system (Electricity Supply Board, 2020).

Electricity demand exhibits peaks, typically occurring in the morning and evening to coincide with meal preparation and media use. The evening peak between 4pm and 8pm represents the highest levels of electricity demand in developed countries with temperate climates such as Ireland and the UK (Anderson & Torriti, 2018). The existence of these peaks, generates significant economic and environmental costs both from direct greenhouse gas emissions and from the production and maintenance of extra generation infrastructure (Avanici et al., 2019).  Electricity cannot be stored efficiently, so the market must clear instantaneously, yet sufficient generation capacity must be available to satisfy peak demand.  Renewable energy is often characterised and constrained by asynchronous generation and weather dependent variability, that creates uncertainty which makes the management of a flexible demand profile difficult. Utilities thus may rely on less efficient and more environmentally damaging but consistent facilities to provide baseload capacity and satisfy peak demand. (US Department of Energy, 2020).

The introduction of smart meters has a range of short and long-term aims. In the short-term, accurate consumption information and dynamic pricing can be used to incentivize shifts in energy usage away from peak times. In the longer term, smart-metering technology can facilitate the development of smart grids, connecting homes and appliances to providers with two-way digital communication. This will enable automated appliances to optimize energy consumption, reducing peak generation capacity requirements and costs for consumers and utilities. Smart grids can also facilitate shifts towards renewable energy sources and local microgeneration (US Department of Energy, 2020).


The dominant cost driver is the capital expenditure on the production and installation of meters themselves. These depend on the type of meter installed, labour costs, and the extent to which existing infrastructure must be improved to facilitate the technology. These factors, and therefore the costs of smart meter installations, are likely to vary considerably between jurisdictions. Significant operational costs include: IT maintenance; network management costs; customer support; and related staff training; (European Commission, 2015).

Early Benefits

Smart meters offer two readily accessible sources of value: accurate and timely information on energy usage; and the facilitation of dynamic pricing.

i. Accurate Information and Billing

Smart meters provide accurate, up-to-date information on energy consumption to both utility providers and to consumers, through digital in-home-displays or through home energy management systems (EMS) accessible via mobile phone apps or online. This immediately removes the need for manual meter readings and estimated bills, reducing operating costs for suppliers and enabling consumers to make more informed choices about their energy needs. (Electricity Supply Board, 2020)

ii. Dynamic Pricing

Smart meters facilitate the introduction of dynamic pricing, a prominent example of which is the Time of Use tariff. Under these tariffs, consumers pay higher prices for electricity during peak times. This aims to incentivize shifts in energy usage away from peak periods, spreading electricity consumption more evenly through the day, thereby reducing peak generation capacity requirements. (Di Cosmo et al., 2014). Faruqui et al (2010) state that the widespread adoption of dynamic tariffs is essential to ensure the short-term cost-effectiveness of smart-metering in Europe.

Advanced Benefits

The vision for smart meter technology is that its advanced benefits will include: smart grid facilitation and development; smart homes and appliances; and the integration of local microgeneration and renewable energy sources into the wider energy grid. (Commission for the Regulation of Utilities, 2017).

i. Smart Grids

The smart grid refers to digital technology which enables two-way communication between utility providers and consumers. This consists of a system of controls, computers, automation and new technologies which respond to information from the electrical grid to regulate energy usage within the home. Smart grids also have potential to enable: more efficient transmission of energy; quicker restoration of power after outages; improved integration of renewable and customer-owned power generation facilities; and ultimately reduced costs for utility providers and consumers. Smart grids can also facilitate the prioritization of supply continuity and restoration during shortages, with the grid being programmed to prioritize electricity for essential locations (e.g. emergency services). (US Department of Energy, 2020)


ii. Smart Appliances/Smart Homes

Smart meters and smart grids can facilitate the development of smart homes and smart appliances. Smart appliances access data from the grid and respond by automatically self-regulating to optimize energy consumption (Avancini et al., 2019). The concept here is that computerized controls in the home and in appliances receive digital signals from the energy provider and respond by minimizing energy use during periods of high demand or shifting energy use to times when power is available at lower prices (US Department of Energy, 2020).


iii. Microgeneration/Local Renewable Energy

It is envisioned that smart meters and the smart grid will support local micro-generation of electricity from renewable sources (e.g. solar panels) (Commission for the Regulation of Utilities, 2019). Smart grids can connect these sources to the wider electricity grid, allowing households to feed energy into grid if surplus electricity is being generated. This could facilitate a credit system for consumers to offset against electricity bills. Advanced development in this context could include dynamic pricing of these credits, with consumers earning more for electricity fed into the grid at peak demand times. Smart meters can provide real-time information to customers on this process and on the level of energy production from micro-generation sources in the home (US Department of Energy, 2020).

Existing empirical literature demonstrates the effectiveness of smart metering and time-of-use tariffs in reducing peak demand for electricity. The European Commission note that smart metering, in conjunction with time-of-use tariffs, can facilitate a 10% reduction in peak electricity demand around Europe (European Commission, 2016). There is “conclusive evidence” from US experiments that such implementation can achieve reductions in peak demand ranging from 3% to 6% (Di Cosmo et al., 2014).

Further evidence supports the potential for advanced value form smart grid and smart appliance development. Harding and Lamarche (2016) and Faruqui et al (2010) find in the USA that households with smart thermostats (which automatically adjust temperature in response to peak pricing) achieve a greater reduction in energy consumption during peak hours than those without one. Kobus et al (2015) offer similar findings in a one-year study of Dutch households – among households with smart meters and dynamic tariffs, those with smart washing machines achieved the greatest demand shift to off-peak periods and to periods when electricity production from the houses’ solar panels was high. This evidence suggests that the appliance automation facilitated by smart grid development can complement the impact of smart meters and dynamic tariffs.

There are some concerns relating to the uptake of dynamic tariffs by consumers, and the potential for energy savings facilitated by smart meters to be offset by other energy-related behaviours. McCoy and Lyons (2017) suggest that energy savings facilitated by time-of-use pricing may be offset by reduced investment in energy saving features in the home. Belton and Lunn (2020) argue that complexity may prevent consumers from switching to a time-of-use tariff, even when it is the cheaper option. Actively informing consumers of potential savings can remedy this, and is important to ensure that smart meter technology is utilised by consumers to deliver savings.

Overall, empirical literature recognises that smart meter technology can facilitate significant shifts in electricity consumption away from peak times. Dynamic tariff structures are an essential element of this.  Informing consumers of the potential benefits of smart meters and how to access them is an important measure to ensure success in this regard. Evidence also suggests that the development of smart grids, smart homes and smart appliances can facilitate automated energy use optimization in the home and can add to the impact of smart meter technology in the transition to a low-carbon energy system.


Anderson, B., & Torriti, J. (2018). Explaining shifts in UK electricity demand using time use data from 1974 to 2014. Energy Policy, 123, 544-557. https://doi.org/10.1016/j.enpol.2018.09.025

Avancini, D., Rodrigues, J., Martins, S., Rabelo, R., Al-Muhtadi, J., & Solic, P. (2019). Energy meters evolution in smart grids: A review. Journal Of Cleaner Production217, pp.702-715. https://www.sciencedirect.com/science/article/abs/pii/S0959652619302501

Belton, C., & Lunn, P. (2020). Smart choices? An experimental study of smart meters and time-of-use tariffs in Ireland. Energy Policy140, 111243. https://doi.org/10.1016/j.enpol.2020.111243

Commission for Regulation of Utilities. (2019). Smart Meter Upgrade The Customer-Led Transition to Time-of-Use. Dublin. https://www.cru.ie/wp-content/uploads/2018/05/CRU19019-Customer-Led-Transition-to-Time-of-Use.pdf

Di Cosmo, V., Lyons, S., & Nolan, A. (2014). Estimating the Impact of Time-of-Use Pricing on Irish Electricity Demand. The Energy Journal35(2). https://doi.org/10.5547/01956574.35.2.6

Electricity Supply Board (2020). Smart Meters: Background [Online]. https://www.esbnetworks.ie/existing-connection/meters-readings/smart-meter-upgrade/background.

European Commission. Directorate General for Energy. (2015) Study on cost benefit analysis of Smart Metering Systems in EU Member States: Final Report. Available at: https://ec.europa.eu/energy/sites/ener/files/documents/AF%20Mercados%20NTUA%20CBA%20Final%20Report%20June%2015.pdf

European Commission. Directorate General for Energy. (2016). Impact Assessment Study on Downstream Flexibility, Price Flexibility, Demand Response & Smart Metering. Luxembourg: Publications Office of the European Union. https://ec.europa.eu/energy/sites/ener/files/documents/demand_response_ia_study_final_report_12-08-2016.pdf

Faruqui, A., Harris, D., & Hledik, R. (2010). Unlocking the €53 billion savings from smart meters in the EU: How increasing the adoption of dynamic tariffs could make or break the EU’s smart grid investment. Energy Policy38(10), 6222-6231. https://doi.org/10.1016/j.enpol.2010.06.010

Harding, M., & Lamarche, C. (2016). Empowering Consumers Through Data and Smart Technology: Experimental Evidence on the Consequences of Time-of-Use Electricity Pricing Policies. Journal Of Policy Analysis And Management35(4), 906-931. https://doi.org/10.1002/pam.21928

Kobus, C., Klaassen, E., Mugge, R., & Schoormans, J. (2015). A real-life assessment on the effect of smart appliances for shifting households’ electricity demand. Applied Energy147, 335-343. https://doi.org/10.1016/j.apenergy.2015.01.073

McCoy, D., & Lyons, S. (2016). Unintended outcomes of electricity smart-metering: trading-off consumption and investment behaviour. Energy Efficiency10(2), 299-318. https://doi.org/10.1007/s12053-016-9452-9

US Department of Energy. Consumer Engagement. Smartgrid.gov. [Online](2020). Available at: https://www.smartgrid.gov/the_smart_grid/consumer_engagement.html Accessed June 23rd 2020.

US Department of Energy. The Smart Home. Smartgrid.gov. [Online](2020). Available at: https://www.smartgrid.gov/the_smart_grid/smart_home.html. Accessed June 23rd 2020.

US Department of Energy. The Smart Grid. Smartgrid.gov. (2020). Available at: https://www.smartgrid.gov/the_smart_grid/smart_grid.html. Accessed June 23rd 2020.


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Policymeasures.com (2020). Smart Meters (Theory). Available at: https://policymeasures.com/measure/smart-meters-theory/. Last accessed: 20-09-2020.