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Establishing the Optimal Tariff in Rural Electricity Distribution Networksy.
KTH, School of Electrical Engineering (EES), Electric Power Systems.
KTH, School of Electrical Engineering (EES), Electric Power Systems.
2009 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Access to electricity is a key factor of improving the living standard in a country, as it enhances the quality of services such as education, health care and productivity. The rural population of Uganda is however only supplied with electricity to a degree less than three percent. There are large financial issues in extending the national electricity grid why small stand alone systems are sometimes a more valuable option. Even then, there are large investment costs that need to be covered by the sale of electricity. Due to the limited buying power of rural consumers, the end-user tariff setting becomes of great significance of the financial outlook. If the tariff is set too high, the consumption will most likely be lower than what it could be, resulting in a loss of revenue as well as inhibiting the improvement of living standards for the consumers. On the other hand, if the tariff is set too low, it could lead to excessive consumption, resulting in power failures.

In view of the above, the main aim of this study has been to investigate the consequences of different tariffs in an isolated rural power system. This was done by studying the electricity consumption in two already electrified rural networks in order to find information on demand behavior and load profiles. Interviews with electricity consumers were conducted to investigate how their demand would change if tariffs were altered.

Demand as a function of price was shown by linear curves indicating the price sensitivity and demand factor, the latter being the theoretical maximum demand when price is zero. These parameters were modelled in Monte Carlo simulations with the aim to predict the demand behavior of a site that is not yet electrified and find the tariff that should be applied to this site. The simulations were based on two potential economic objectives of how to operate the system; by altruistic or profit maximising means.

Depending on whether the system is altruistic or profit maximizing, the optimum point of tariff differs. In the altruistic case, this tariff should be set where the costs are covered by the revenues. The profit maximizing system instead requests the tariff where profit is as high as possible. Furthermore, two different structures of tariff setting were tested for the system; a structure with time-of-use levels where the tariff should be higher during the peak demand hours of the day, and a flat rate structure where the tariff is constant throughout the day.

The field study showed that, on average, the price sensitivity factor of domestic consumers were slightly higher than of the commercial consumers. The results also showed that the majority of the commercial consumers reside in the same building as their business. Furthermore, rural consumers exhibit low awareness of their consumption patterns and the price of electricity. Extensive information from the distribution companies to the customers is therefore essential to maintain a sustainable electricity consumption, as it enables consumers to make rational decisions about their electricity consumption and opt for more efficient alternatives.

A financial analysis for the specific case study was also conducted from simulations. The analysis found for an altruistic system a tariff slighly lower tariff than the tariff applied in the national grid today. However, the system will require an additional financing to cover the payments before the year when revenues exceed expenses, but can be paid back within eight years. The tariffs found by simulating with a profit maximizing system operator are more than twice as high as the current tariff applied in the national grid today. On the other hand, the system requires a very small additional loan or subsidy compared to the altruistic simulations and has a pay-off time within six years.

Place, publisher, year, edition, pages
2009. , 97 p.
EES Examensarbete / Master Thesis, XR-EE-ES 2009:005
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
URN: urn:nbn:se:kth:diva-119253OAI: diva2:610181
Educational program
Master of Science in Engineering - Industrial Engineering and Management
Available from: 2013-03-12 Created: 2013-03-08 Last updated: 2013-03-12Bibliographically approved

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