ABSTRACT The Online Biomass Potential Atlas is a tool primarily intended for geo-visualisation of biomass data from the Biomass Potential Monitoring System in Bosnia and Herzegovina. However, its role does not have to end here. By developing a functional extension, it can offer an environment for the location analysis of potential biomass users and sources of unused biomass potential. This paper describes an approach for developing tool with such functionality, based on spatial interaction modelling. Determining the optimal location for biogas plants in the region covered by the administrative units of two cantons in Bosnia and Herzegovina is considered as a case study. Based on the analysis conducted in the case study, the feasibility of applying this tool has been demonstrated.

Electrical power systems throughout the world experience an unprecedented transformation. One of the main motivation for this is a transition from conventional power generation technologies towards renewable energy sources (RES). This transformation has numerous positive effects on power systems, environment and social engagements on a global level. However, poorly planned and allocated RES add complexity to power systems operations and can cause numerous challenges. This paper investigates some of the most common parameters used in the RES grid integration process. In particular, the impact of different PV penetration levels on energy losses and transformer current loading in a PV predominated power system are presented. The analysis is performed in DigSILENT® Powerfactory software using quasi-dynamic analysis on a modified IEEE 14 bus system. The results demonstrated that the energy losses could be reduced until the critical point of PV penetration. After the critical point is reached, the energy losses start to grow rapidly. The current loading of the transformers also tend to reduce with the increase in PV penetration until the critical point and rapidly grow after the critical point. In conclusion, results presented in this work demonstrate the importance of appropriate RES integration planning and analysis, which remains an important engineering task.

The increasing integration of renewable energy resources into distribution systems promotes microgrids as important and emerging network concept. The coordination control between the photovoltaic (PV) generator and the battery energy storage system (BESS) is required to provide a necessary amount of active power in the system. Method for voltage regulation for a microgrid which consists of a PV generator with the maximum power point tracking (MPPT) control and BESS in the stand-alone mode of operation is presented in the paper. MATLAB/Simulink is used to perform all simulation studies. The validity of the proposed methods is clearly verified on the model of a real distribution network, which might be operated as a microgrid. Obtained results demonstrate that the suggested method can be used for effective voltage regulation in microgrid applications, which remains a vibrant field of research.

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.

The ascending trend in retail electricity prices since the first energy package is often blamed on the market reforms and the cost of a low carbon economy. The present study analyzes EU-28 statistical data on retail prices for the medium households and industries, for the years 2008–2017, a period of economic slowdown. We focus especially on six countries, Austria, Denmark, Germany, Greece, Latvia and Spain and examine retail prices against the degree of renewable energy sources (RES) penetration and the market liberalization in each country. We also examine the cases of three Western Balkan countries that still have a very low degree of liberalization. The increasing percentage of RES in electricity generation, the number of retailers and the market share of the main retailer are analyzed with respect to the retail electricity price for the period studied. In spite of the different specifics of each country's economy, there are certain common trends. The price increase has been found to be the result of levies and taxes, rather than the energy cost, with the burden carried mostly by households. In the cases studied here, the increase correlates with either the increase in RES or increased competition or both depending on the market structures in place during the examined period and the maturity and performance of the measures towards a liberalized electricity market and a low carbon economy.

This paper proposes an application of the Teager Energy Operator (TEO) next to the Discrete Wavelet Transform (DWT) and Hilbert-Huang Transform (HHT) for the power system fault identification, localization, and classification. Several fault types are simulated in the NE 39 bus test system using DigSILENT Power Factory software. Frequency and voltage signals are obtained and analyzed through the optimal placement of the Phasor Measurement Units (PMUs). The performance and the comparison of the applied techniques are assessed through a large number of the simulated faults for each fault type. The promising results obtained in the TEO analysis highlights the appropriate application of this technique in the power system fault detection. Value of the presented work reflects in the clear fault detection process. For the specified test system 141 fault simulations are made, frequency and voltage signals obtained from 13 measuring points in the system and analyzed with different signal processing techniques providing time, location and type of the fault.

Noninvasive load monitoring have been investigated by researchers for decades due to its cost-effective benefits. Upon introduction of smart meters, obtaining data about power consumption of households became easier. Numerous different techniques have been applied on the power consumption data to gain useful information out of it. This study applies machine learning techniques (Bayes network, random forest and rotational forest) to determine the operation state of households, where households are assumed to be either in ON or OFF state. Tracebase power consumption signature repository was used to train and test proposed machine learning models. Tracebase dataset was preprocessed to generate 4 different datasets. Test results have shown that these machine learning algorithms are able to estimate operation state with high accuracy and Bayes network shows outstanding performance among them with overall accuracy of 95%. Proposed method is extremely cost-effective for load monitoring and could replace some of the physical sensors in the smart houses.

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.

This paper presents an algorithm for Distributed Generation (DG) allocation planning, using fuzzy set theory and fuzzy multi criteria decision making based on the Bellman-Zadeh method. The proposed model considers power losses and investment deferral values on one side (goals) and line currents, node voltages and short circuit power values on the other (constraints). The objective of this work is to create a model which can be used to provide the best tradeoff between conflicting goals and constraints, inherently present in the DG allocation problem. The model is flexible and can be easily extended to include additional goals and constraints. In order to demonstrate its relevance, the proposed algorithm is applied to the realistic middle voltage (35 kV) distribution system in Bosnia and Herzegovina, in which various DG siting and sizing alternatives are considered. Results indicate that the algorithm is applicable to realistic systems and capable to provide valuable information regarding the influence of various DG allocation alternatives on system variables. It is expected that the algorithm will contribute towards the improvement of the existing DG planning process and it will be used in practical situations by planning engineers from the Utilities and Regulators.

In a power distribution network, network topology information is essential for an efficient operation of the network. This information is not accurately available, due to uninformed changes that happen from time to time, or uncertain meter readings. Reliable prediction of system status is a highly demanded functionality of smart energy systems, which can enable users or human operators to react quickly to potential future system changes. This paper presents potential of artificial neural networks to determine missing power meter readings in medium voltage (MV) networks where the number of on-line measurements is limited, and state estimation relies heavily on estimates of power injections. The applicability of the approach is demonstrated through simulation using supervisory control and data acquisition and smart meter measurements recorded from an actual MV distribution network. Results are showing that artificial neural networks can have 100% measurements detection accuracy.