Usefulness of the article : The deployment of smart grids that enable the monitoring and control of different energy flows, as well as renewable energies in the energy mix, is changing the energy network. This note presents how these technologies, in particular smart meters and decentralized production, enable consumers to both control their consumption and actively participate in energy production. Without changes in energy tariffs, the deployment of these technologies is limited by the financial capabilities of the operator.
Summary:
- European directives aimed at increasing energy savings are based primarily on the deployment of smart grids. These are one of the priorities of the energy transition.
Smart meters (e.g., Linky in France) give consumers access to their raw consumption data so they can better control their consumption.- Empirical studies show that smart meters bring about lasting changes in certain consumption patterns, but they could also cause a rebound effect and increase total energy consumption.
The deployment of decentralized production based on solar panels allows consumers not only to consume their own energy, but also to feed their surplus production into the grid.- However, the system for compensating for surplus energy in the grid could put the operator in financial difficulty. This effect can be reduced through the deployment of complementary technologies such as electric vehicles.
Smart grids are defined bythe International Energy Agency ( IEA, 2011) as modern networks that enable the monitoring and optimization of interconnected elements in the electricity market. The deployment of these smart grids is in accordance with European Directives 2019/944 and 2018/2002, which aim to increase energy efficiency and consumer information. Their deployment is the result of growing energy demand, aging infrastructure, and the deployment of renewable energies (RE) and electric vehicles.
These new technologies enable consumers to participate « actively » in their consumption by adapting their consumption needs to market prices. They can even lead to self-consumption or feeding the grid thanks to decentralized production technologies. A new category of consumers is thus emerging: the « prosumer »—a neologism derived from the English word prosumer[1], which combines producer/professional and consumer in a single word. According to Enedis (2020), since March 2020, there has been a decrease in energy consumption by SMEs/SMIs and professionals, linked to Covid-19, in contrast to an increase in consumption by individuals. If practices such as teleworking continue to be encouraged in the long term, we could see a « transfer » of energy consumption from industry to households (Ambec and Crampes, 2020; Hook et al, 2020). This would make it essential for households to control their own consumption.
Figure 1: Consumption by segment from 2008 to June 2020
Source: Enedis (2020)
With this in mind, this note presents the extent to which smart grids enable consumers to control their consumption and participate in energy production as « prosumers. »
1. Response to demand
In most markets, prices signal the scarcity of a good or resource[2]. Historically, the electricity market has been characterized by a fixed price[i] for consumers. Furthermore, unlike other markets, the current configuration of the electricity market does not allow consumers to store electricity: thus, consumers are subject to a fixed price for electricity based on the amount consumed, but do not participate « actively« in the electricity market. Ideally, consumers should adapt their consumption over time based on market information (Ambec and Crampes, 2020). However, in this specific market, this is particularly difficult since the wholesale price varies constantly depending on production and consumption is spread throughout the day.
The significant deployment of intermittent renewable energies in the energy mix also makes it increasingly difficult to guarantee a fixed price for consumers without automatically resorting to fossil fuels, whose reliable production is independent of weather conditions. As a result, some slightly more flexible contracts exist[iv]: these are based on the intensive deployment of smart meters. These meters provide access to raw consumption data via online platforms, which could enable individuals to optimize their consumption based on real-time prices.
Demand response, considered the holy grail ( Léautier, 2019) in the electricity market, is defined as the ability of consumers to modify their electricity consumption based on wholesale prices. According to Léautier (2019), demand response is now technically possible thanks to two major technological advances:
- On the one hand, since the 2000s, the organization of the wholesale market has made it possible to access wholesale prices at short intervals (one day in advance).
- On the other hand, smart meters allow consumers to access this information and thus shift or advance their consumption according to prices (for example, thanks to programmable equipment such as washing machines, vacuum cleaners, dishwashers, heating, air conditioners, etc.). This task would be made easier if smart meters were accompanied by smart personal assistants (e.g., Google Home, Amazon Echo, Apple Home Pod, etc.), allowing remote control of consumption during potential price spikes.
However, this optimization is costly in terms of equipment and time, and assumes that consumers have the necessary skills to control their consumption. This could limit the rollout of smart meters and programmable equipment to the entire population. According to Ambec and Crampes (2020), smart meters would have a positive impact on consumers, the economy, and the environment. However, the costs would be high in relation to the benefits, with a significant percentage of individuals equipped (and therefore responsive). This is especially true given that a high percentage of responsive consumers would lead to redistribution problems. Indeed, wealthier households would be more likely to optimize their consumption (Mills and Schleich 2010, Brounen et al. 2013). Their average consumption would be higher (Ambec and Crampes, 2020) and they would derive greater benefits from optimizing their consumption. Income level would also be an important factor in understanding the raw consumption data that would enable them to change certain behaviors. Therefore, consumers exposed to a high fixed price would tend to be low-income households.
Furthermore, Ambec and Crampes (2020) believe that it would be more beneficial to promote equipment upgrades among industrial and commercial consumers. Since equipment prices are constant, the potential benefits are proportional to consumption. Rivers (2018) presents the empirical literature on demand response, as well as a case study in Ontario. These studies include quasi-experimental methods and randomized experimentation (the main difference being that the treatment group in quasi-experimental studies is the result of public policy or self-selection): these studies show that the deployment of smart meters seems to reduce household consumption overall. Access to their consumption data would enable consumers to make lasting changes to certain consumption patterns. For example, by significantly reducing their consumption during peak hours (Jessoe and Rapson, 2014) in favor of lower prices during off-peak hours. On the other hand, they would be less responsive to small price variations during the day (Andersen et al., 2017; Rivers, 2018). In contrast, Dato et al. (2020) highlight the possibility of a rebound effect from smart meters. For real-time prices that are low enough during off-peak periods, net consumption would increase compared to fixed prices: thus, smart meters would not necessarily lead to energy savings—and therefore increased energy efficiency.
Beyond smart meters, decentralized means of production could enable consumers not only to better control their consumption, but also to feed energy back into the grid.
2. Production, transmission, and distribution
New forms of production and consumption (e.g., individual and collective self-consumption, storage, and smart meters) based primarily on renewable energy sources are changing the electricity grid. Historically, the electricity grid was designed to connect centralized production fields to consumers. The deployment of renewable energy sources, whose production depends on climatic (e.g., hydroelectric power plants) and meteorological (e.g., wind and solar farms) variables, also requires a grid that is capable of constantly adapting—i.e., coping with peaks in production and consumption while avoiding power outages (i.e., intermittency). In terms of transmission and distribution, two major challenges arise in terms of flow control: microgrids (e.g., collective self-consumption, smart cities, etc.) and electric vehicles (Cretì and Fontoni, 2019).
The deployment of decentralized production (DP) technologies, such as photovoltaic panels, enabling individual and collective self-consumption, requires a network capable of handling energy flows in both directions.
Figure 2: Number of decentralized solar production installations
Source: Enedis (2020)
PD offers consumers the possibility of self-consumption of their own production. The introduction of this technology benefits from renewable energy support policies such as feed-in tariffs and calls for tenders (CRE, 2018). However, large-scale deployment of PD requires strong coordination between production units, cost optimization, and self-healing of the grid using information and communication technology (ICT) tools. Operating this grid is costly, and the peak consumption or production hours that allow costs to be recouped are limited. This situation can be exacerbated depending on the compensation mechanism in place. Gauthier et al (2018) compare two smart metering mechanisms, with one or two meters. The first measures net imports (net metering), and the second measures imports and exports separately (net purchasing).
Figure 3: Metering mechanisms in Europe
Source: Gauthier et al (2018)
According to existing studies (Borenstein and Bushnell, 2015; Gauthier et al, 2018), net metering puts the network operator in financial difficulty as it is no longer able to recover these operating costs. As this revenue depends on the amount transported by the network from producers to consumers, if the latter reduce their net consumption through self-consumption, the operator may be forced to increase its tariffs. This leads to an increase in electricity rates to the detriment of traditional consumers[vii]: This is the concept of the » death spiral « (Borenstein and Bushnell, 2015). On the other hand, De Groote et al. (2016) show that, overall, high-income households are the most likely to install photovoltaic panels. Thus, as with smart meters, decentralized production leads to redistribution problems. The deployment of other technologies could nevertheless reduce this problem: Hoarau and Perez (2019) show that network operating costs could be offset by electric vehicles. In fact, the wealthiest households are largely the main users of this type of vehicle: nevertheless, in order to recover the network’s operating costs, consumers should be able to optimize their charging and discharging times according to electricity prices and network needs. On the other hand, net purchasing would allow certain EU member states to establish a tariff for prosumers feeding electricity into the grid (Gauthier et al, 2018), thereby reducing the operator’s losses somewhat.
The intensive deployment of all these technologies, which enable consumers to reduce their consumption and produce energy themselves, can facilitate the creation of microgrids —which bring together nearby renewable production, storage, and consumption units that can be connected to a traditional grid ( » macrogrid « ). These have the advantage of being able to be connected to the grid at a single point, so that they can be isolated in the event of disruptions to the traditional grid. Microgrids therefore offer greater local reliability and reduce the load on grid control, but require significant local cooperation and coordination.
Conclusion
The deployment of smart grids requires significant investment in technology and consumers who are able to optimize their energy consumption/production. Moretti et al (2017) compared 17 studies and concluded that, on average, the costs of deploying these smart grids exceed the benefits (however, this analysis is based on a large number of assumptions[3]). On the other hand, there is consensus on their positive impact in terms of energy efficiency and greenhouse gas emissions reduction.
Nevertheless, concerns about redistribution are emerging: wealthier consumers seem to be better able to optimize their consumption at the expense of fixed rates. In addition, they are also the ones who make the most use of decentralized production and electric vehicles.
In the public interest, it is therefore necessary to study new electricity tariffs that allow for the recovery of network operating costs and reduce the negative effects on the least affluent households. This remains difficult today, as equipment levels vary greatly from region to region (Rivers, 2018), and gradual deployment limits the ability of public authorities to observe long-term effects.
References
Ambec. S., & Crampes, C. (2020).Real-time electricity pricing to balance green energy intermittency, TSE Working Paper, n. 20-1087.
Ambec. S., & Crampes, C. (2020). Covid-19: infected electricity markets. TSE Op-ed. https://www.tse-fr.eu/covid-19-infected-electricity-markets
Andersen, L-M., Hansen, L-G., Jensen, C-L., & Wolak, F. (2017). Using Real-Time Pricing and Information Provision to Shift Intra-Day Electricity Consumption: Evidence from Denmark, manuscript, Stanford University.
Borenstein, S., & Bushnell, J. (2015). The US electricity industry after 20 years of restructuring. Annual Review of Economics, 7, 437–463.
Brounen, Dirk & Kok, Nils & Quigley, John M. (2013).Energy literacy, awareness, and conservation behavior of residential households, Energy Economics, Elsevier, vol. 38(C), pages 42-50.
CRE (2018). Self-consumption. https://www.cre.fr/Transition-energetique-et-innovation-technologique/Autoconsommation
Cretì, A., & Fontini, F. (2019). Economics of Electricity: Markets, Competition and Rules. Cambridge: Cambridge University Press. doi:10.1017/9781316884614
Dato, P., Durmaz, T., & Pommeret, A. (2020). Smart grids and renewable electricity generation by households, Energy Economics, 86: 104511.
De Groote, O., Pepermans, G., & Verboven, F. (2016). Heterogeneity in the adoption of photovoltaic systems in Flanders. Energy Economics, 59, 45–57.
Enedis. (2020). Monthly Analysis of the Enedis Electricity Balance.
Gautier, A., Jacqmin, J., & Poudou, J. (2018). The prosumers and the grid. J Regul Econ 53, 100–126 . https://doi.org/10.1007/s11149-018-9350-5
Hoarau, Quentin & Perez, Yannick, 2019. Network tariff design with prosumers and electromobility: Who wins, who loses?, Energy Economics, Elsevier, vol. 83(C), pages 26-39.
Hook, A., Court, V., Sovacool, B-K., & Sorell, S. (2020). A systematic review of the energy and climate impacts of teleworking. Environmental Research Letters.
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Jessoe, K., & Rapson, D. (2014). Knowledge Is (Less) Power: Experimental Evidence from Residential Energy Use, American Economic Review 104(4): 1417-1438.
Mills, B. & Schleich, J. (2010). What’s driving energy efficient appliance label awareness and purchase propensity? Energy Policy, 38: 814-825.
M. Moretti, S.N. Djomo, H. Azadi, K. May, K. De Vos, S. Van Passel, N. Witters. (2017). A systematic review of environmental and economic impacts of smart grids. Renew . Sustain. Energy Rev., 68, pp. 888-898
Léautier, T-O. (2019). Imperfect Markets and Imperfect Regulation: An Introduction to the Microeconomics and Political Economy of Power Markets, MIT Press.
Rivers, N. (2018). Leveraging the Smart Grid: The Effect of Real-Time Information on Consumer Decisions – OECD Environment Working Paper no. 127.
[1] Sociologist Alvin Toffler first used this term in 1980—without necessarily referring to the electricity market—to describe prosumers as a direct result of the introduction of information and communication technologies.
[2] This is Léon Walras’ concept of scarcity: the more useful and limited in quantity a good or service is, the higher its value.
[3] The time horizon and discount rate
[i] Independent of supply during the consumption period
[ii] Consumers do not observe the wholesale price of electricity or the level of production (or production technology) and cannot store electricity. Thus, their consumption cannot be optimized according to supply.
[iii] These contracts offer prices that vary according to the duration of use and off-peak/peak hours (e.g., EDF’s Blue Tariff, Engie’s Elec Weekend, etc.).
[iv] Energy savings linked to better control of consumption would reduce dependence on fossil fuels.
[v] Historically, the grid has distributed centralized production to consumers. The introduction of PD technologies means that my consumers are in turn becoming « prosumers » who feed energy back into the grid.
[vi] Consumers
[vii] Not equipped with decentralized production.
[viii] Indeed, the latter generate additional revenue for the grid: however, if the energy tariff is too beneficial for decentralized production, the incentives for deploying electric vehicles are reduced.
