Experimental data of total wind generation, recorded at 5 minute intervals and published by the Bonneville Power Administration for the years 2007 to 2013, were analyzed on a year by year basis. All data were normalized to total installed power of wind plants. Statistical distribution functions were obtained for the following wind generation-related quantities: total generation as percentage of total installed capacity; change in total generation power in 5, 10, 15, 20, 25, 30, 45, and 60 minutes as percentage of total installed capacity; duration of intervals with total generated power, expressed as percentage of total installed capacity, lower than certain pre-specified level. The statistical distributions obtained from the data were used to devise simple, yet accurate, theoretical models. The models presented here can be utilized in analyses related to power system economics/policy, because they describe availability of wind energy resource in simple statistical terms relevant to interactions of wind generation with electricity system, and electricity markets. After a brief display of the models, the article concentrates on static properties of the observed system's electricity generation related to its capacity credit, as well as on dynamic properties related to the demand for fast regulation (i.e., secondary and fast tertiary reserve). Both properties are important for technical planning of future electricity systems, as well as rational design of policy measures.

Minimisation of variability of energy delivered from a group of wind plants into the power system using portfolio theory approach was studied. One of the assumptions of that theory is Gaussian distribution of the sample, which is not satisfied in case of wind generation. Therefore, optimisation of a "portfolio" of plants with different goal functions was studied. It was supposed that a decision on distribution of a fixed amount of generation capacity to be installed among a set of geographical locations with known wind statistics is to be made with minimised variability of generation as a goal. In that way the statistical cancellation of variability would be used in the best possible manner. This article is a brief report on results of such an investigation. An example of nine locations in Croatia was used. These locations' wind statistics are known from historic generation data.

Cable parameter extraction is needed for many practical purposes. The main motivation for this research came from a need to extract high-frequency properties of cables used for indoor electricity distribution because they can be utilized for transmission of communication signals, too, by means of the power-line carrier (PLC) technology. These cables have relatively high specific losses, so that indoor broadband PLC applications can use frequencies up to some 30 MHz. In this paper, cable features in the frequency range from 5 to 30 MHz, belonging to the high-frequency band, were studied. The purpose of this paper is to describe a practical method by which the cable parameters in the mentioned indicated frequency range can be extracted rather accurately from measured complex reflection coefficients at a cable end when the other end is terminated by precise open and short terminations, using very simple equipment. The parameters extracted in this way can be used for very precise modeling of real-life multiterminal distribution networks.

We discuss an approach to minimizing the group variability of generation in a system of intermittent renewable sources using the portfolio theory. The total variability of a system that can be modeled using various parameters as goal-functions is minimized given any desired level of expected long-term generation. An extensive analysis was carried out on a set of time series of measured generation data obtained from twenty wind plants in Croatia over five years in one hour and fifteen minutes, time resolutions. The choice of the goal function most relevant for the operational (and economic) consequences of short-term variability is discussed.

This paper presents a survey design methodology aimed at mitigating central tendency and acquiescence biases, which are commonly encountered in traditional Likert-scale surveys. The proposed approach employs a forced ranking method, using a case study involving 220 engineering students at the University of Zagreb as a source of data to assess whether respondents exhibit patterns of systematically avoiding honest answers for any reason. Statistical analysis demonstrates the effectiveness of the design in reducing these biases. The results provide strong evidence that the method developed in this article can minimize response distortions without sacrificing data richness. While the primary focus is on the methodological aspects, the case study illustrates the potential of this approach for ethical and attitudinal research. The study concludes with recommendations for refining survey techniques and exploring their broader applicability in different populations and contexts.