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.

The paper analyses the coordinated hydro-wind power generation considering joint bidding in the electricity market. The impact of mutual bidding strategies on market prices, traded volumes, and revenues has been quantified. The coordination assumes that hydro power generation is scheduled mainly to compensate the differences between actual and planned wind power outputs. The potential of this coordination in achieving and utilizing of market power is explored. The market equilibrium of asymmetric generation companies is analyzed using a game theory approach. The assumed market situation is imperfect competition and non-cooperative game. A numerical approximation of the asymmetric supply function equilibrium is used to model this game. An introduced novelty is the application of an asymmetric supply function equilibrium approximation for coordinated hydro-wind power generation. The model is tested using real input data from the Croatian power system.

This study analyzes the short-run hydro generation scheduling for the wind power differences from the contracted schedule. The approach for construction of the joint short-run marginal cost curve for the hydro-wind coordinated generation is proposed and applied on the real example. This joint short-run marginal cost curve is important for its participation in the energy markets and for economic feasibility assessment of such coordination. The approach credibly describes the short-run marginal costs which this coordination bears in “real life”. The approach is based on the duality framework of a convex programming and as a novelty combines the shadow price of risk mitigation, which quantifies the hourly cost of mitigating risk, and the water shadow price, which quantifies the marginal cost of electricity production. The proposed approach is formulated as a stochastic linear program and tested on the case of the Vinodol hydropower system and the wind farm Vrataruša in Croatia. The result of the case study is a family of 24 joint short-run marginal cost curves. The proposed method is expected to be of great interest to investors as it enables risk mitigation for investors with diverse risk preferences, from risk-averse to risk-seeking.

The trend toward increasing integration of wind farms into the power system is a challenge for transmission and distribution system operators and electricity market operators. The variability of electricity generation from wind farms increases the requirements for the flexibility needed for the reliable and stable operation of the power system. Operating a power system with a high share of renewables requires advanced generation and consumption forecasting methods to ensure the reliable and economical operation of the system. Installed wind power capacities require advanced techniques to monitor and control such data-rich power systems. The rapid development of advanced artificial neural networks and data processing capabilities offers numerous potential applications. The effectiveness of advanced deep recurrent neural networks with long-term memory is constantly being demonstrated for learning complex temporal sequence-to-sequence dependencies. This paper presents the application of deep learning methods to wind power production forecasting. The models are trained using historical wind farm generation measurements and NWP weather forecasts for the areas of Croatian wind farms. Furthermore, a comparison of the accuracy of the proposed models with currently used forecasting tools is presented.

This study analyses the short-run hydro generation scheduling for the wind power differences from the contracted schedule. The approach for construction of the joint short-run marginal cost curve for the hydro-wind coordinated generation is proposed and applied on the real example. This joint short-run marginal cost (SRMC) curve is important for its participation in the energy markets and for economic feasibility assessment of such coordination. The approach credibly describes the short-run marginal costs which this coordination bears in “real life”. The approach is based on the duality framework of a convex programming and as a novelty combines the shadow price of risk mitigation capability and the water shadow price. The proposed approach is formulated as a stochastic linear program and tested on the case of the Vinodol hydropower system and the wind farm Vrataruša in Croatia. The result of the case study is a family of 24 joint short-run marginal cost curves.