The importance of electricity in everyday life and demands to improve the reliability of distribution systems force utilities to operate and plan their networks in a more secure and economical manner. With higher demands on reliability from both customers and regulators, a big pressure has been put on the security of electricity supply which is considered as a fundamental requirement for modern societies. Thus, efficient solutions for reliability and security of supply improvements are not just of increasing interest, but also have significant socio-economic relevance. Distribution system planning (DSP) is one of the major activities of distribution utilities to deal with reliability enhancement.

This thesis deals with developing optimization algorithms, which aim is to min- imize customer interruption costs, and thus maximize the reliability of the system. This is implemented either by decreasing customer interruption duration, frequency of customer interruptions or both. The algorithms are applied on a single or multi- ple DSP problems. Mixed-integer programming has been used as an optimization approach.

It has been shown that solving and optimizing each one of the DSP problems contributes greatly to the reliability improvement, but brings certain challenges. Moreover, applying algorithms on multiple and integrated DSP problems together leads to even bigger complexity and burdensome. However, going toward this inte- grated approach results in a more appropriate and realistic DSP model.

The idea behind the optimization is to achieve balance between reliability and the means to achieve this reliability. It is a decision making process, i.e. a trade-off between physical and pricing dimension of security of supply.