The importance of the accurate forecasting of power outputsof wind-based generation systems is increasing, as their contributions to the total system generation are rising. However, wind energy resource exhibits strong and stochastic spatio-temporal variations, which further combine with the uncertainties in WF operating regimes, i.e., numbers of wind turbines in normal operation, under curtailment, or that are faulty/disconnected. This paper presents a novel approach for efficient dealing with uncertainties in hour-ahead forecasted WF power outputs. It first applies Bayesian convolutional neural network-bidirectional long short-term memory (Bayesian CNN-BiLSTM) method, which allows for a more accurate probabilistic forecasting of wind speed, air density and wind direction, using the nearby WFs as additional input data. The WF operating regimes are also predicted using the same Bayesian CNN-BiLSTM structure. Afterwards, a high-dimensional Vine-Gaussian mixture Copula model is combined with Bayesian CNN-BiLSTM model to evaluate uncertainties in the WF outputs based on a cross-correlational conditioning of the forecasted weather variables and operating regimes. The proposed combined model is applied and validated using the actual field measurements from two WF clusters in close locations in Croatia and is also benchmarked against several other models.

The most commonly used method for including photovoltaic system in the control of system frequency is to operate a PV power plant at reduced power while maintaining a specific amount of available power reserve. Such operating regime, often referred to as de-loaded, can cause additional costs and, consequently, determining a required power reduction becomes vitally important. This paper introduces a method for determining the required minimum de-loaded reserve of a photovoltaic system. To achieve the desired result a parabolic approximation of the system frequency response is developed to derive an explicit function of the frequency nadir. Taking this approximation into consideration, a method for determining the required minimum de-loaded margin was developed and tested in a case-study which observed a small power system consisting of several power plants. To demonstrate the accuracy of the proposed parabolic approximation, the calculated approximate values of the frequency nadir were compared to the values of the frequency nadir obtained from the frequency response simulations performed on the power system model developed in MATLAB/Simulink.

This paper presents a high-level overview of the integration of renewable energy sources (RES), primarily wind and solar, into the electric power system (EPS) in Croatia. It presents transmission system integration aspects for the particular case of this country. It explains the current situation and technical characteristics of the current conventional generation units and currently installed wind energy capacities. Based on the current situation future development scenario is determined and used to evaluate the impacts of the wide-scale integration of renewables. Grid connections aspects, power balancing, market participation, and inertia reduction aspects are considered. Furthermore, some specifics of both solar and wind integration are discussed identifying problems and potential solutions. Primarily through the provision of the inertial response of both solar and wind and through better forecasting of wind production. Finally, the outlook for the Croatian power system is given, that will most probably double its RES capacity in the coming 3-year period and a certain level of investments and changes of current operational practices will need to be provided.