On designing cost-reflective demand tariffs

Passey et al. (2017) used actual energy consumption data of Sydney households to analyse the extent to which different electricity network demand tariffs charge customers according to the cost of their consumption. Among other things, they illustrate that a demand tariff that charges households’ individual monthly peak demand is less likely to be cost-reflective than one applied during the monthly network peak period. 

Picture by Nastya Dulhiier, Unsplash

Electricity network demand tariffs - what are they?

Electricity bills are mainly paid to cover the costs of the energy system. Among these costs, network costs refer to the expenses associated with the maintenance and building of physical assets such as transmission lines and substations. In Great Britain, they account for roughly a quarter of a typical household's electricity bill.

Network costs are recovered through a combination of various charges, such as daily and volumetric rates. Demand tariffs are those that only apply during periods of high electricity demand, and part of their purpose is to signal to customers that their consumption is more costly during these times. After all, since the grid must be built to handle maximum load, higher peak demands often translate into more network investments.

What are the benefits of cost-reflective pricing?

With cost-reflective pricing, customers are charged according to their consumption’s contribution to total costs. For example, if a household’s consumption during peak hours results in additional spending for network reinforcements, that household would be charged for this cost. Consequently, it incentivises customers to consume the maximum amount of energy for which they are willing to pay the supply cost. Such an approach can be appealing because, in theory, it 1) prevents wasteful investments, and 2) ensures that companies recover their expenses.

A shortcoming of cost-reflective pricing is that customers may be unwilling to pay for the cost of their energy, not because they don't need it, but because they are in fuel poverty. For such customers, it can be desirable to subsidise their bills by raising those of others. As a result, cost-reflective pricing isn’t the right choice everywhere. But it remains a key benchmark for assessing economic efficiency and the extent to which customers pay according to their cost to the system.

On charging household-level, versus network-level, monthly peaks

Passey et al. (2017) used actual energy consumption data of 3,876 Sydney households to compare different demand tariffs in terms of cost reflectivity. This short article only focuses on two of these: a demand tariff that charges households’ individual peak demand, and a tariff that charges households’ contribution to the network’s monthly peak demand.

To assess the degree of cost reflectivity, the researchers estimated the correlation between households’ charged demand under each tariff (i.e. unitised demand), and their contribution to the annual network peak periods (i.e. coincident demand). After all, the closer the relationship between charged volumes and system peak contributions, the easier it is for the tariff to be charged in a cost-reflective manner. 

Methodologically, for the first tariff, the researchers defined unitised demand as the weighted average of each household’s individual monthly peak demands. For the second tariff, they estimated unitised demand by calculating each household’s average contribution to the network’s monthly peak demands. Furthermore, they defined coincident demand as the household’s average contribution to the year’s eight highest demand periods (i.e. system peak demand). Notice that the monthly network peak periods don’t always coincide with the system peak periods, partially because you can have multiple system peaks in the same month, for example, during winter and summer.

Passey et al. (2017) analysed the relationship between households’ unitised and coincident demands, and they concluded that a demand tariff that charges household-level peaks would likely be less cost-reflective than one that charges monthly network peaks. More specifically, they estimated Pearson’s correlation coefficient to be 0.80 for the first tariff and 0.90 for the second. Figures 1 and 2 below illustrate the relationship between households’ unitised and coincident demands for each tariff type.

Figure 1: Unitised versus coincident demand when charging households’ individual monthly peak demand

Source: Passey et al. (2017)

Figure 2: Unitised versus coincident demand when charging households’ contribution to the monthly network peak demand

Source: Passey et al. (2017)

Conclusion

The discussed results are unsurprising. After all, mathematically, households’ monthly demand peaks cannot be more correlated with coincident demand than monthly network peaks. The paper’s contribution stems from its approach to cost-reflective pricing, instead of its results. More specifically, following the authors’ methodology, policymakers can calculate unitised and coincident demands for different demand tariffs. These, in turn, can be used to assess the extent to which tariffs are cost-reflective, informing policy-making.