As more and more final consumers are metered hourly, the volume of meter values that grid operators have to collect, store, and transmit to other actors in the electricity market continues to grow. But hourly metering does not have to be a burden for grid operators - the growing volumes of meter values can be analyzed in multiple ways to provide grid operators with valuable insights that allow them to make better decisions.

New insights through analysis of hourly meter values

Magnus Linden | Sweco

Sweco has developed several new analysis methods with which large volumes of meter data are used to gain new insights for grid operators to run more efficient operations. For instance, the company has developed methods to analyze network losses, as well as methods to analyze the financial impact of the introduction of demand-response, solar-based micro-generation, or various different tariff schemes.

In one recent project, Sweco helped a Swedish grid operator to evaluate the financial consequences of offering load tariffs[1] to customers that are currently offered tariffs based on fuse size[2], something that has become possible as more and more consumers are measured on an hourly basis. As consumers whose load is weather-dependent are moved to load tariffs, revenue streams from these consumers might change – even monthly payments might be replaced by payments that are much higher during winter months than during the rest of the year. Sweco therefore carried out an analysis to see if a proposed load tariff would generate the same total revenues as existing tariffs, how revenues were distributed over the year, and how revenues were affected by weather.

Even though the goal of the analysis was to compare one particular load tariff with the existing fuse-based tariff, the analysis method actually made it very easy to compare the existing tariff with a whole set of potential load tariffs. In general, the methodology is well suited for comparing different scenarios using the large volumes of meter values that will become available as more and more consumers are metered on an hourly basis.

The project started by collecting detailed meter values between j January and August 2012 for a set of consumers that could, in the future, be offered load tariffs. These meter values were checked for accuracy and cleaned. To make the conclusions as general as possible, some of the meter values were modified to take weather variations into account. The processed meter values were then entered into Lavastorm Analytics’ platform for analysis. Lavastorm Analytics can handle very large data sets and can be easily programmed to simulate many different kinds of business processes. In this particular project, Lavastorm’s platform was programmed to simulate the financial effects of offering different network tariffs.

The project concluded that the proposed load tariff would reduce revenues by 10% compared to revenues from the current fuse-based tariff. The analysis showed that even though a switch would yield the grid operator higher revenues in the winter, the revenues during the rest of the year would be lower, and overall a switch would lead to significantly reduced revenues. These findings were true both for raw meter values and for meter values that had been modified to take weather variations into account. The figure below shows the outcome for modified meter values:

Figure 1: Replacing the existing tariff with a load tariff for a selected set of customers would reduce revenues from these customers by 10.2 %.

The specific load tariff selected by the grid company was only one of many load tariffs that were studied in the project. A scenario was set for each one of these studied tariffs. All scenarios shared one reference tariff: it was fuse-based and composed of an annual fixed fee of $1,030and a variable energy fee of 2.4 cents per kWh consumed. The selected load tariffs had a fixed annual fee of $134, a load component that varied between $2.98 and $4.47 per kW, and an energy component that varied between 1.86 cents to 3.63 cents per kWh. In all, revenues for 36 different load tariffs were computed and compared against the revenues from the reference tariff.

The analysis was performed for both raw and weather-corrected meter values, and the results were presented in a result matrix with one row per load component and one column per energy component. Each cell thus represents one specific load tariff and each cell contained the difference in revenues between that tariff and the reference tariff. Each cell was color coded to make it easy to interpret the results. The table below shows the results for weather-corrected meter values:




Transmissions cost (¢/kWh)



















Power cost ($/kW)


















































Figure 2: Revenue differences between a reference tariff and a set of load tariffs.

The analysis was performed using meter values for a specific customer group. By repeating the analysis for larger sets of customers, it should be possible to draw informed conclusions about the general effects of different tariffs on revenue. Because grid operators in Sweden are free to set their own tariffs but their revenues are regulated, it becomes important to find the correct price levels. The methodology developed by Sweco can be used to compute tariffs and price levels that will yield the revenues grid operators are allowed to have.

The methodology has also been used to analyze the effects of installing equipment that automatically cuts power if load exceeds certain thresholds. Load interrupters can be used by some consumer categories to reduce costs, and can also be beneficial for grid companies, as its use tends to even out loads in distribution grids. Using large data sets of hourly meter values it becomes possible to compute the financial effects of introducing this technology.

Finally, the methodology has been used to study how increasing volumes of micro-generation will affect grid company revenues. As the use of micro-generation grows, the electricity that needs to be distributed to end consumers falls, while the amount of electricity injected into local grids may rise if enough micro-generation facilities are large enough to allow their owners to, at times, export excess electricity. A study in which information about solar radiation was combined with hourly metering showed that grid company revenues would fall if very small-scale solar-based micro-generation facilities become common, whereas grid company revenues would rise if medium-scale facilities become common and consumers started to inject excess electricity into the grid.

In this article we have shown how large datasets of hourly meter readings can be used to perform analyses that give grid operators new insights that can be used to reduce costs and increase revenues. A tool such as Lavastorm Analytics can be used to analyze large datasets with an accuracy that rivals that of larger and more expensive systems. Sweco has used this methodology to provide services to individual grid companies, and it is currently using the methodology to help the Swedish electricity markets regulator study the effects of various tariff structures.

[1] A load tariff is a tariff where the monthly payment is proportional to the maximum monthly load

[2] Tariffs based on fuse size consist of a fixed fee and an energy fee, both of which are proportional to the voltage level at which the customer is connected.


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