Study of the Intelligent Control and Modes of the Arctic-Adopted Wind–Diesel Hybrid System
Abstract
:1. Introduction
- Limitations in equipment and fuel delivery due to the short periods in which transportation is possible;
- The need for quick installation and construction without the use of heavy lifting and transport equipment in the absence of roads;
- The possibility for maintenance without the involvement of qualified specialists.
- The presentation of advanced control that includes climatic forecasting (wind speed, icing, etc.);
- The presentation of a new control approach to limit the effects of wind turbine icing.
2. Materials and Methods
- In real time, maximize the energy output of the wind power plant and fuel economy while covering the required load;
- Provide remote monitoring of the hybrid system’s parameters and operating modes;
- Provide intelligent dispatching of the equipment, ensuring the maximum degree of autonomy;
- Monitor the condition of the equipment, analyze the statistics of wind–diesel operating modes, and provide forecasting of the wind regime;
- Ensure scheduling of equipment operation, maintenance, risk assessment, and emergency prevention interventions;
- Duplicate the main controller of the system and the control and measuring systems; in an emergency, the possibility of manual control should be provided;
- Be adaptable and supply energy around the clock, including in the event of a failure of the generating equipment.
- An equipment diagnostics unit (supervisory control and data acquisition of each system element);
- A power balance control unit that distributes energy between the system’s generating equipment;
- A forecasting unit;
- An icing prediction unit.
2.1. Power Balance Control and Equipment Diagnostics Units (Standard Control)
2.2. Forecasting and Icing Prediction Units (Advanced Control)
- Statistical methods (GAMLSS);
- Machine learning (XGBoost, Random forest);
- Combined systems [30].
3. Results
3.1. Hybrid System Modes
- (1)
- Load following mode: In this mode, the diesel generator outputs electricity in accordance with the load (leading mode). The surplus electricity is first used to charge the battery and then to heat the water using the dump load. Disconnection of the diesel component is possible in the case of a fully charged battery and a prolonged excess of wind turbine output over the load. The diesel component is switched on when the battery voltage reaches the specified minimum. Thus, the battery works in deep cycles during periods of high winds. The dump load, together with the battery, contributes to the regulation of the network voltage to achieve its stable operation and acts as a buffer for load hesitation. Surplus energy is utilized in the form of useful heat for heating needs. Figure 6 shows hourly balances of power in supervisory control and data acquisition (SCADA)-based monitoring data over five days. From the figure, it is noticeable that for the majority of the time when the wind turbine is operating, the power balance significantly exceeds the load.
- (2)
- Cycle charge with short-term forecasting mode: In this mode, the diesel generator works as an additional source of energy to cover power shortages. In the case of favorable wind turbine output forecasts, it is switched off. At the same time, the battery is used more efficiently and the size of the buffer capacity of the dump load is reduced. The changes in dump load performance are visible when comparing Figure 6 and Figure 7. From Figure 7, it can be seen that the diesel generator is repeatedly replaced by the battery discharge.
3.2. The Effect of Pitch and Tip-to-Speed Ratio Control
4. Conclusions
- The article presents the architecture of an advanced intelligent automatic control system for a wind–diesel hybrid system with high renewable energy penetration and describes the main modes of its operation.
- The integration of the wind turbine and battery into the hybrid system enabled fuel savings of 38%, which were achieved by replacing the power generated by the diesel engine with wide turbine and battery power. In the load following mode, it was possible to disconnect the diesel generator when the battery was fully charged and wind turbine production was high. The dump load and the battery were used to regulate the network voltage. The excess electricity produced was used for heating.
- With the addition of wind speed forecasting (LSTM model) and the cyclic charge mode, the share of wind turbine energy going to the secondary dump load decreased to 38%, and diesel fuel savings increased to 60%. Overall, the fuel savings correspond to the effects of significant additions of either wind turbine or battery capacities.
- The net savings of using pitch and tip-to-speed ratio control exceed the cost of installing this system for medium- and high-capacity wind turbines. The use of the icing prediction unit in conjunction with weather forecasting and the turbine control system provides a more reliable operation of the wind turbine in harsh climatic conditions. It is estimated that these systems can reduce the operational expenditure (OPEX) by approximately 20%.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Parameter | Condition |
---|---|
Wind Speed | >3 m/s |
Temperature | −4 °C > T > −20 °C |
Relative Humidity | >95% |
Wind Speed (m/s) | Cpclean | λ | α (°) | Cpicing | Cpαλ | λopt | Cpα | αopt (°) |
---|---|---|---|---|---|---|---|---|
2.2 | 0.4755 | 5.6 | 5 | 0.4157 | 0.4526 | 4.6 | 0.4499 | 0 |
3.2 | 0.4565 | 4.7 | 4 | 0.3495 | 0.3848 | 2.8 | 0.3597 | 9 |
6 | 0.5126 | 8.3 | 5 | 0.3821 | 0.4340 | 3.9 | 0.3907 | 6 |
7.3 | 0.4984 | 7.0 | 6 | 0.3461 | 0.4078 | 3.2 | 0.3564 | 5 |
19.2 | 0.4173 | 3.5 | 2 | 0.3829 | 0.4002 | 3.1 | 0.3989 | 5 |
Parameter | Diesel Only (Simulation) | WDDP Load Following Mode (Field) | WDPP Cycle Charge with Short-Term Forecasting Mode (Simulation) |
---|---|---|---|
DG energy output, kWh | 3804 | 2423 | 1591.0 |
WTG energy output, kWh | 0 | 3302 | 3302 |
Battery roundtrip output, kWh | 0 | 450 | 937 |
Battery 80%, DOD cycles | 0 | 4 | 9 |
DG start/stop cycles | 1 | 3 | 13 |
DG specific fuel consumption, g/kWh | 313 | 304 | 302 |
Fuel consumption, liters | 1417 | 876 | 573 |
DG average load, % | 31.5 | 35 | 31.5 |
Fuel savings, l | 0 | 540 (38%) | 844 (60%) |
Dump load Energy consumption, kWh | 0 | 1598 | 1247 |
Location | Latitude | Longitude | Production Loss (%) | Icing Atlas Production Loss (%) |
---|---|---|---|---|
Olhava | 65° N | 25° E | 7.1 | 5.9 (4.6) |
Madetkoski | 68° N | 27° E | 12.4 | 9.3 (4.0) |
Jääräjoki | 70° N | 28° E | 6.3 | 8.4 (1.5) |
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Elistratov, V.; Konishchev, M.; Denisov, R.; Bogun, I.; Grönman, A.; Turunen-Saaresti, T.; Lugo, A.J. Study of the Intelligent Control and Modes of the Arctic-Adopted Wind–Diesel Hybrid System. Energies 2021, 14, 4188. https://doi.org/10.3390/en14144188
Elistratov V, Konishchev M, Denisov R, Bogun I, Grönman A, Turunen-Saaresti T, Lugo AJ. Study of the Intelligent Control and Modes of the Arctic-Adopted Wind–Diesel Hybrid System. Energies. 2021; 14(14):4188. https://doi.org/10.3390/en14144188
Chicago/Turabian StyleElistratov, Viktor, Mikhail Konishchev, Roman Denisov, Inna Bogun, Aki Grönman, Teemu Turunen-Saaresti, and Afonso Julian Lugo. 2021. "Study of the Intelligent Control and Modes of the Arctic-Adopted Wind–Diesel Hybrid System" Energies 14, no. 14: 4188. https://doi.org/10.3390/en14144188