The issue of backup for intermittent renewable sources comes with the relatively low capacity value and the very limited contribution to generation security that such sources have. However, beside standard compensation measures (power system flexibility, positive and negative reserve, etc.), inherent natural properties of wind and solar power resources can play a certain role as well. This work builds upon previous analyses and gives a quantitative system non-specific data assessment of individual power generation scenarios (dispersed power generation, hybrid solar-wind power generation, etc.), based on one year data records available for three sites of Bosnia-Herzegovina. The scope of this work is to statistically evaluate and compare the contribution of each case scenario to the required power system backup margin and the associated capacity value for the selected resource. It has thereby been found that for the given data dispersed wind generation exceeds the effects of a hybrid wind-solar scenario, however, positive effects were found to come with a mixed dispersed wind-solar power generation as well. The capacity value assessment resulted in improved properties of the output reliability, but only up to a limited capacity factor of the wind only scenario.

Abstract: In this paper, the results of correlations between air temperature and electricity demand by linear regression and Wavelet Coherence (WTC) approach for three different European countries are presented. The results show a very close relationship between air temperature and electricity demand for the selected power systems, however, the WTC approach presents interesting dynamics of correlations between air temperature and electricity demand at different time-frequency space and provide useful information for a more complete understanding of the related consumption. Key words: power system, electricity demand, air temperature, linear regression, wave-let coherence 1. Introduction The electric power system is a very complex system. It is composed of a large number of different elements such as generators, lines, transformers, etc., but it also has a lot of different consumption categories such as household, industrial, transportation, and others. Short, me-dium or long-term planning requires thorough understanding of consumption characteristics which are defined by their load curves. The load curves represent characteristics of the power system’s load variability as a function of time, and their analysis can be performed for some customer groups, some geographic areas or the power system as a whole, where it is very important to identify the main factors affecting the consumption of electricity, such as growth and structure of Gross Domestic Product (GDP), demographic change, housing standard, the

In this paper, the relationship between the Gross Domestic Product (GDP), air temperature variations and power consumption is evaluated using the linear regression and Wavelet Coherence (WTC) approach on a 1971-2011 time series for the United Kingdom (UK). The results based on the linear regression approach indicate that some 66% variability of the UK electricity demand can be explained by the quarterly GDP variations, while only 11% of the quarterly changes of the UK electricity demand are caused by seasonal air temperature variations. WTC however, can detect the period of time when GDP and air temperature significantly correlate with electricity demand and the results of the wavelet correlation at different time scales indicate that a significant correlation is to be found on a long-term basis for GDP and on an annual basis for seasonal air-temperature variations. This approach provides an insight into the properties of the impact of the main factors on power consumption on the basis of which the power system development or operation planning and forecasting the power consumption can be improved.

Intermittent renewable generation plays a significant role in power system planning, hence the requirement for more exact data and resource representation for power system modeling is of high interest. It is therefore the aim of this paper to analyze properties of the wind resource in specific operating conditions. As Bosnia-Herzegovina (B&H) abounds with mountain plateaus at high altitudes, the paper will analyze data records of four recording sites located at altitudes ranging from 1250m to 1700m a.s.l. Due to the specific geographic position and topography, the country experiences different climate conditions (Mediterranean, Moderate Continental and Alpine Climate) within a rather small surface area, which also has an effect at the recording sites of interest. The rate of change of diurnal patterns by season is analyzed by means of the Normalized Root-Mean-Square deviation (NRMS), whereby occurrence and intensity of occurring patterns is analyzed by means of the Fast Fourier Transform (FFT). The paper finds interesting results on wind statistics and the related operating conditions, with particular focus on mean diurnal wind profile and variability and the rate of change by season.

The current generation capacity structure of the Public Power Company Elektroprivreda B&H (EP B&H) of 70%:30% in favour of TPPs provides some advantages like safe and reliable supply, but promoting RES and their use in the generation portfolio of the company is a commitment in order to contribute to sustainable development plans and environmental preservation. The ongoing measurement campaign performed by EP B&H investigates wind and solar energy potential on the territory of B&H. This creates preconditions for techno-economic evaluations of exploiting wind and solar power, with the final aim of building wind power plants (WPP) and photovoltaic power plants (PVPP) in the country. Particularly in terms of wind power, high altitude abandoned areas are assessed for potential WPP construction. Experience from the three year measurement campaign has shown promissing results in the available wind and solar potential of B&H, providing good preconditions for future techno-economic assessments and planning activities. Keywords: wind potential, solar potential, harsh weather conditions

Intermittent renewable generation plays a significant role in power system planning, since the demand-following nature of a power system's operation and control requires steady supply of electricity at any given time in order to maintain system stability. A realistic representation of the wind power output in the time domain may therewith become critical for an accurate assessment of impacts and security risks involved. To tackle the issue this paper aims at giving a brief statistical analysis of Bosnia-Herzegovina's (B&H) wind potential. The goal is to present and evaluate large sampled data sets in a form representative of their natural system boundaries. With reference to evaluation methodologies summarized in literature, focus will be placed on wind resource representation and quantitative assessment of its capacity value in a “non specific” power system. The paper gives a comprehensive, system non specific quantitative data assessment, based solely on physical behavior and the consequential characteristics relevant to system requirements, in a form applicable to different power system planning scenarios.

This paper presents a combined Genetic Algorithm - Fuzzy Optimization approach to the Unit Commitment problem. The Unit Commitment problem is a high complex combinatorial optimization task, nonlinear and large-scale. In order to obtain a near optimal solution in low computational time and storage requirements, with respect to all specified constraints, a Genetic Algorithm using real-coded chromosomes is proposed in opposite to the more commonly used binary coded scheme. Gathering data from a list of strict priority order the Genetic Algorithm generates different candidate solutions to the problem, whereas Fuzzy Optimization guides the whole search process under an uncertain environment (varying load demand, renewable energy sources). A system consisting of 10 generating units is presented to demonstrate application of the proposed algorithm to the Unit Commitment problem. The obtained results show satisfactory outcome in total cost, compared to Dynamic Programming based applications and the sole Genetic Algorithm based solution to the Unit Commitment problem.