Both the fixed and energy distribution tariff cover almost 50 % of the total revenue in the residential customers case. Hence, we can analyse three main scenarios and the effects on the revenue and distribution tariffs. In the basic scenario, electrical energy remains at the same level but the power loads increase. Basically, in this case, the distribution revenue remains at the same level with the present tariff structure. In the worst case scenario, the energy consumption decreases by 14 % and the power increases by 13 %. This scenario is the most challenging one from the distribution revenue perspective, because the revenue would decrease with the present tariff structure and prices. The energy consumption will increase by 15 %, and the power is forecasted to increase by 5 % in the high energy scenario. The high energy scenario is a good option for DSOs, because the energy consumption grows and changes in the power loads do not necessarily pose challenges. Generally, the level of costs is increasing, and this would mean that the prices of energy and the fixed tariff should be raised in any case.
In practice, the worst case scenario may be challenging if the energy consumption decreases. It will decrease the DSO’s revenue with the present tariff structure. Thus, it is necessary to raise the distribution prices to earn at least the same revenue as today. The pressure to price increases may be even higher because of the increasing power demand and level of costs. If the energy consumption decreases by 14 %, it can be calculated how much the distribution prices have to be increased in order to earn the same amount of revenue:
If the price increase is focused on all types of energy tariffs, the price increase is 16 %. In practice, this would mean that the energy prices have to be raised by 0.0028–0.0045 €/kWh to earn the same revenue as today.
In the case where the price increase is focused on all types of fixed tariffs, the price increase is 13 %. This would mean that fixed charges have to rise by 1.7–
3.0 €/month per customer.
If the price increase is made equally for all tariffs, it would mean that the price increase would be about 7 %.
It can be concluded that if the revenue is affected by energy consumption, it is reasonable to focus the pressures of price increase on fixed tariffs. This solution decreases the dependence of revenue on energy consumption, and thus, it makes the revenue more constant. The negative side is that the customers’ opportunities to influence the distribution charges will decrease with the present tariff principle, because fixed tariffs are based on the main fuse size.
6.4
Implications of the case impactsNetwork location will have effects on changes in the electricity end-use in the future. The changes may vary between DSOs and network areas, especially between rural and urban areas. All changes will take place over a long-time period. In addition, the results are
temperature normalized, which means that variation in outdoor temperature between different years may cause extra fluctuation to the results. The case network environment is a typical Finnish electricity distribution environment. The case area includes rural and population centre areas. The forecasts and parameters related to the forecasting process such as outdoor temperature are based on average values, because the values have to represent long-term values, and the modelling of the network loads has to be reasonable from the perspective of energy and power. This guarantees that the results are at a medium level. The results can be different in urban areas, because for instance the customer structure is different. However, the results show where the electricity distribution business is heading.
The LTLF involves various uncertainties, which cannot be completely eliminated.
However, the scenario approach can take different future alternatives into account, and the scenarios can be updated on an annual basis, which produces more information of the network area and possible load changes in the area. This case study has shown that future energy technologies will have a significant impact on distribution network loads, energy, and power, and the DSO’s revenue in the long term. The effects of the technologies depend on many issues, as was mentioned above. The most important question is how technologies will take place in the operating environment. However, it has to be borne in mind that volume- and consumption-related factors like the number of people will have significant effects on loads.
The methodology has been tested with residential customers, and the results in the case area are only based on forecasts of residential customers. However, the greatest impacts on network loads may be found in the groups of residential customers, because they are typically the largest customer group and may be willing to acquire new technologies (Leenheer et al., 2011) and (Annala et al., 2012). In rural areas, the loads will develop differently than in urban areas. In this work, the main focus is on rural areas, which have a lot of residential customers. Further, new technologies may have more radical impacts on the loads in the countryside.
Traditionally, the network load forecasts have been based on energy forecasts, which are converted into power forecasts by load models. Energy forecasts are typically based on the estimation of the annual growth in energy consumption. For instance, the present network planning tools apply the approach of regularly increasing annual load consumption. Load forecasts are based on energy forecasts and load models. If energy is forecasted to increase constantly, there is a similar trend also with power. Consequently, different annual load growth percentage estimates for the highest mean hourly power in the case area have been applied. This is illustrated in Figure 6.30. In the figure, the annual electrical energy and the highest mean hourly power are estimated to increase with three different growth percentages in the case area. In addition, the annual energy forecasts with the new long-term load forecasting process are indicated in the figure. It can be seen that the forecast produce totally different forecasting results. The results of the new long-term load forecasting on the highest mean hourly powers are also indicated in the figure.
The loads will increase in every scenario, but there are a great differences in the forecasts.
6.4 Implications of the case impacts 171
However, the power results may be totally dissimilar at the lower network levels with the new load forecasting process because of the future energy technologies. Thus, we may conclude that the effects of the future energy technologies cannot be forecasted by the previous method; further, errors in electrical energy forecasts will have significant impacts on the distribution business.
Figure 6.30. Forecasting of the future electrical energy and the highest mean hourly power with the annual energy consumption growth percentage and with the new load forecasting process in the case area.
This proves that electricity end-use will change significantly and that previous load forecasting and modelling methods are no longer valid. We may conclude that it is impossible to reliably forecast loads in different areas with the previous methodologies.
The previous methodologies cannot take into account the impacts of microgeneration and other future energy technologies. Further, they may lead to different conclusions of the future network loads and can be problematic from the network planning perspective.
This section has shown that changes in the electricity end-use may pose challenges for network and business planning. Several variables have to be taken into account when considering the future business environment. The main challenges in the electricity distribution operating environment are that electricity end-use deviates from the previous consumption trends. In addition, more challenges may arise as a result of major changes in the number of customers and the structure of livelihood. Electricity end-use, energy, and power may vary considerably in the future. This may reduce incomes and require large investments by the DSOs. Therefore, the increasing costs and peak loads may lead to challenging and problematic situations. This is an extremely undesirable situation from the perspective of the distribution business. Therefore, new methods to manage the
New forecasting process energy New forecasting process power Low-energy scenario energy Low-energy scenario power High-energy scenario energy High-energy scenario power