*3.1. Hybrid System Modes*

A comparative analysis of the operating modes of an existing Arctic wind–diesel hybrid system was carried out to assess the practical benefits of the implementation of intelligent control algorithms. The correlation between the structure of the considered equipment and high renewable energy penetration is illustrated in Figure 5.

**Figure 5.** Structural scheme of the wind–diesel hybrid system.

The model used is compiled in Python (Python Software Foundation, Gemini Dr., Beaverton, OR, USA), on the core of a real wind–diesel power plant (WDPP) control system. The system has the following properties: a wind turbine of 100 kW, a full capacity converter, a diesel generator set of 110 kW, a battery energy storage system with a capacity of 200 kWh, a dump load with a capacity of 70 kW, and a real load graph (max 55 kW). The limitations of the model are as follows. Firstly, loading the initial data does not take into account the delay in their download. Secondly, the authors do not investigate the influence of the model operation speed on the signals of real facilities.


is reduced. The changes in dump load performance are visible when comparing Figures 6 and 7. From Figure 7, it can be seen that the diesel generator is repeatedly replaced by the battery discharge.

**Figure 6.** Power balance under the load following mode (SCADA measurements).

**Figure 7.** Power balance under the cycle charge and short-term forecasting mode (simulation).

A more detailed comparison of the results is summarized in Table 3. The integration of the wind turbine and battery into the system during the analysis period facilitated fuel savings of 38%; however, to ensure the stable operation of the power system, a significant portion of the electricity generated by the wind turbine (48%) was distributed to the secondary regulatory load. With the wind turbine production forecasts and battery operation functioning in a cyclic mode, the share of wind turbine energy going to the secondary control load decreased to 38% and renewable energy penetration increased to 60%. At the same time, the number of battery cycles increased 2.2 times, up to two cycles of 80% charge/discharge per day (660 cycles per year). In their research employing similar system components (diesel generator, wind turbine, and battery), Elkadeem et al. [5] were able to reduce diesel fuel consumption by 85% (compared with a diesel-only system); however, their study had a significantly larger relative share of wind power capacity than the current study (more than two times the diesel generators' power). When compared with the load following mode, doubling the share of wind turbines seems to also roughly double the fuel savings. In relative terms, Li et al. [6] employed approximately similar diesel generator and wind power capacities but had significantly higher battery capacity (roughly three times higher). Their study reports a fuel saving of 74%. This leads to the conclusion that the proposed cycle charge with short-term forecasting mode can offer fuel-saving benefits by adding significantly more wind power or battery capacity without adding any actual new capacity.


**Table 3.** Operation statistics over five days.

#### *3.2. The Effect of Pitch and Tip-to-Speed Ratio Control*

To verify the chosen wind turbine icing modeling approach, the performance values of Table 2 were used to build power curves for clean and icing cases. The results of the modeling were then compared with the predictions from the Finnish Icing Atlas, which is based on the Finnish Wind Atlas [39] and ice aggregation modeling according to standard ISO 12394:2001. Since the figures in the Finnish Icing Atlas are reasonably sensitive to location, an area with a radius of 30 km was used to determine the maximum production loss values in each area for comparison with the exact location values presented in parentheses to illustrate local variations. The comparison reveals that the model presented in this work generally overestimates losses (Table 4). The magnitude of the predicted losses is, however, still similar to those achieved. This indicates that the proposed model can produce reasonable estimates for icing effects even though it is based on single airfoil data rather than data for full turbine blade shapes.

**Table 4.** Icing model comparison between the developed model and the Finnish Icing Atlas.


To implement the developed models in the energy system modeling tool, three curve fits were built based on the Madetkoski data from Finland. Figure 8 presents the power curves for a clean case, icing case, and pitch and tip-to-speed (optimized) control case and illustrates how the applied control approach can affect turbine performance. The total positive effect of optimized control on turbine power below the nominal operating point is between 2% to 5%. For wind turbines of medium and high capacity in Arctic zones, the optimized control system is advisable since the total cost of its installation is less than the total economic savings it can achieve. However, for wind turbines with a capacity of less than 300 kW, the installation of such a system must be confirmed by the relevant technical and economic analyses.

**Figure 8.** Wind turbine power curve under clean, icing, and optimized conditions.
