Abstract Power systems around the world have undergone a number of important organizational, structural and technological changes over the past few decades; they are a direct consequence of the electricity market liberalization and transition from conventional energy conversion technologies towards renewable resources. These changes represent many advantages as well as challenges for the Distribution System Operator (DSO). The aim of this paper is to review the most important principles, objectives and technical criteria used in planning the development of the electricity distribution network. Presented principles can be used as basic guidelines when developing short-term and long-term plans for the construction and reconstruction of power distribution facilities. This paper also presents a methodological approach to the planning and ranking of proposed electricity facilities with an example from practice that is based on the real planning problem in ED Mostar. The basic conclusion of the paper is that the identification of objectives, criteria and the application of an appropriate and unique methodology is of the utmost importance for formulating the framework of the planning process.

This paper presents a method for distributed generation (DG) allocation in low voltage distribution network based on the total annual energy loss reduction and Artificial Neural Network (ANN). The proposed method is applied to the PV solar based DG allocation problem in the low voltage distribution network using realistic network data and measurements. This research is motivated by numerous realistic issues faced by the Distribution System Operator in the area of DG planning. The main objective of this work is to develop, test and validate a robust method for DG allocation which can be used in practical problems without the need for extensive system modelling and load flow analysis. The results confirm the importance of appropriate DG planning and show that the proposed method can be used as a promising tool for efficient and effective DG allocation in low voltage distribution network.

The use of Distributed Generation (DG) throughout the world increasing. DG siting and sizing is an important engineering consideration, which is inherently influenced by the system load and DG power output uncertainties. This paper presents research results of the uncertainty influence on DG allocation problem. This influence is investigated using a constrained optimization problem for power loss reduction. The optimization is performed using Genetic Algorithm. The power system load and DG power output uncertainties are addressed using a possibilistic (α – cut method). The algorithm is applied to realistic distribution system to demonstrate its practical relevance. Results indicate that DG can reduce losses. and that uncertainties play a major role in final optimisation results. This paper contributes to the existing knowledge by applying, to a realistic test power system, a DG allocation method, which considers the influence of load and generation uncertainties on optimization results.

This paper presents a new algorithm for distribution system reconstruction planning based on Mamdani type fuzzy inference and BellmanZadeh multi criteria decision making method. The proposed algorithm takes system attributes as inputs (number of customers served by renewed infrastructure, energy losses, power demand and cost of investment) and returns crisp output values which are used as planning criteria. The aim of this paper is to provide a logical decision making framework which can be used to model, evaluate, and rank projects according to required criteria. The proposed model is flexible and can be extended to include additional planning criteria. The proposed method is tested on a realistic distribution system to demonstrate its relevance. It is expected that this paper will make a contribution toward more effective management of power distribution network planning process and that it will be used by planning engineers in practical problems.

The modern power system operation is faced with numerous challenges related to the power quality improvements such as identification and classification of power distribution network (PDN) faults. The recent advances in the area of signal processing allow the development of new algorithms and methods which can be used for fault identification and classification in PDN. This study presents a comparison of two approaches for identification and classification of high-impedance faults (HIFs) in medium-voltage PDN. The first approach is based on the voltage phase difference algorithm, whereas the second approach is based on the combination of discrete wavelet transform and artificial neural networks algorithm. The proposed algorithms are tested on models of a real distribution network, which represents a typical PDN currently used in Bosnia and Herzegovina. It was demonstrated that the proposed methods are capable to accurately detect and classify HIF in PDN. This study makes a contribution to the existing body of knowledge by developing, testing and comparing two methods for HIF classification and identification, whose application represents an improvement when compared with the capability of the existing protection devices.

Identification and classification of high-impedance faults (HIFs) in electric-power distribution systems (EPDSs) represent some of the most significant challenges faced by the distribution system operators (DSOs). The recent advances in signal processing and changes in the EPDS regulatory framework have prompted acceleration in the development of advanced methods used for fault identification and classification in EPDS. The paper presents a method for identification and classification of HIFs in medium-voltage (MV) EPDSs, based on the Discrete Wavelet Transform and Artificial Neural Networks. The method was tested on generated signals based on a real EPDS and it was demonstrated that it is capable to accurately detect and classify HIFs in EPDS. The paper contributes to the existing research by developing and testing, on a real EPDS, a HIF-identification and classification method which offers a better performance compared to the currently installed protection devices.