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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.

Transient overvoltages generated by lightning strikes or switching operations represent a significant risk to bushings and windings of power transformers. They cause stress on the insulation system and can, over time, cause dielectric failure and damage to power transformers. Many transformer failures are reported as dielectric failures and they are not necessarily linked to any particular event when they occur but may be the result of prior damage from transient overvoltage events. Lightning and switching overvoltage waveforms appearing at transformer terminals in real operating conditions may significantly differ from standard impulse voltage waveforms used during laboratory testing. The number and amplitudes of overvoltages which stress the insulation depend on various parameters such as the lightning strike density in the considered area, since it determines how often the transformer is stressed by lightning overvoltages. Since the overvoltage amplitudes at transformer terminals are usually unknown, an on-line overvoltage transient recorder can be used with the ability to sample, analyse and store transients in real-time. In this paper, an on-line transient overvoltage monitoring system (TOMS) for power transformers is presented that is capable to continuously record in real-time various kinds of transient overvoltages such as lightning or switching overvoltages. Special attention is paid to lightning caused transient overvoltages recorded at the terminals of 150 MVA power transformer. Recorded waveforms originating from lightning strikes to overhead lines are correlated with data from the lightning location system (LLS) and supervisory control and data acquisition (SCADA) system. Collected data about overvoltage stresses can be used as the basis for the assessment of the transformer insulation condition, estimation of health index and for analysis of various kinds of events such as faults or equipment failures.