An Arctic Wind-Diesel Power Plant with an Intelligent Automatic Control System
Keywords:
wind energy, wind-diesel power plant, intelligent control, Arctic, active-adaptive links, machine learning
Abstract
The main problem faced in supplying power to remote consumers is that there are high logistical costs of delivering fuel and equipment for diesel power plants, poor density of transport infrastructure and, as a consequence, a high cost of fuel. There are also high operating costs at diesel power plants and specific fuel consumption; in addition, there is no monitoring and control automation. In view of a high wind potential of the Arctic territories, energy complexes and systems can be effectively modernized and constructed on the basis of modular wind-diesel power plants with an intelligent control system. A concept of and hardware solutions for an intelligent automatic control system are proposed, the use of which makes it possible to maximize the amount of electricity generated by renewable sources owing to dynamically redistributing the power between the hybrid energy complex components and, as a result, to minimize fuel consumption. An analysis of a controlled wind-diesel power plant has shown that by using an intelligent automatic control system, forecasting the electricity generation by a wind power plant, and arranging storage battery operation in a cyclic mode, it becomes possible to effectively cover the load schedule of an autonomous consumer and increase the extent of diesel fuel substitution by up to 60 % or more.
References
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#
1. Elistratov V.V. Vozobnovlyaemaya energetika (Renewable Energy). SPb.: Izd-vo politekhn. un-ta, 2016, 424 p.
2. Projected Costs of Generating Electricity 2020, IEA, Paris [Electron. resource], URL: https://www.iea.org/reports/projected-costs-of-generating-electricity-2020 (Date of appeal 8.12.2021).
3. Kazem H.A., et al. Optimum design and evaluation of hybrid solar/wind/diesel power system for Masirah Island. – Environment, Development and Sustainability, 2017, 19 (5), pp. 1761–1778, DOI:10.1007/s10668-016-9828-1.
4. Elistratov V.V., et al. The Application of Adapted Materials and Technologies to Create Energy Systems Based on Renewable Energy Sources under Harsh Climatic Condition. – Applied Solar Energy, 2018, 54(6), pp. 472–476, DOI:10.3103/S0003701X18060087.
5. Elistratov V., et al. Study of the Intelligent Control and Modes of the Arctic-Adopted Wind–Diesel Hybrid System. – Energies, 2021, 14, 4188, DOI:10.3390/en14144188.
6. Elistratov V.V., Denisov R.S. Energetic and Ecological Justification of RE-hybrid Systems for Vulnerable Ecosystems. – IOP Conf. Series: Earth and Environmental Science, 2021, 689, DOI:10.1088/1755-1315/689/1/012017.
7. Ansari M.M.T., Sangoden V. DMLHFLC (Dual Mode Linguistic Hedge Fuzzy Logic Controller) for an Isolated Wind-Diesel Hybrid Power System with BES (Battery Energy Storage) Unit. – Energy, 2010, 35(9), pp. 3827– 3837, DOI:10.1016/j.energy.2010.05.037.
8. Vachirasricirikul S., Ngamroo I., Kaitwanidvilai S. Coordinated SVC and AVR for Robust Voltage Control in a Hybrid Wind-Diesel System. – Energy Conversion and Management, 2010, 51, pp. 2383–2393, DOI:10.1016/j.enconman.2010.05.001.
9. Elistratov V.V., Bogun I.V., Kasina V.I. Optimization of Wind-Diesel Power Plants Parameters and Placement for Power Supply of Russia’s Northern Regions Consumers. – 16th Conference on Electrical Machines, Drives and Power Systems (ELMA), 2019, 8771647, DOI: 10.1109/ELMA.2019.8771647.
10. Shukur O.B., Lee M.H. Daily Wind Speed Forecasting Through Hybrid KF-ANN model based on ARIMA. – Renewable Energy, 2015, 76, pp. 637–647, DOI:10.1016/j.renene.2014.11.084.
11. Aasim, Singh S.N., Mohapatra A. Repeated Wavelet Transform Based ARIMA Model for Very Short-Term Wind Speed Forecasting. – Renewable Energy, 2019,136, pp. 758–768, DOI:10.1016/j.renene.2019.01.031.
12. Carpinone A., et al. Markov Chain Modeling for Very-Short-Term Wind Power Forecasting. Electric Power Systems Research 2015, 122, pp. 152–158, DOI:10.1016/j.epsr.2014.12.025.
13. D’Amico G., et al. Managing Wind Power Generation via Indexed Semi-Markov Model and Copula. – Energies, 2020, 13, 4246, DOI:10.3390/en13164246.
14. Marugán A.P., et al. A Survey of Artificial Neural Network In Wind Energy Systems. – Applied Energy, 2018, 228, pp. 1822–836, DOI:10.1016/j.apenergy.2018.07.084.
15. Santhosh M., Venkaiah C., Vinod Kumar D.M. Ensemble Empirical Mode Decomposition Based Adaptive Wavelet Neural Network Method for Wind Speed Prediction. – Energy Conversion and Management, 2018, 168, pp. 482– 493, DOI:10.1016/j.enconman.2018.04.099.
16. Huang C., et al. Short Term Wind Speed Predictions by Using the Grey Prediction Model Based Forecast Method. – IEEE Green Technologies Conference (IEEE-Green), 2011, DOI: 10.1109/GREEN.2011.5754856.
17. Gobiet A., et al. Operational Forecasting of Wet Snow Avalanche Activity: a Case Study for the Eastern European Alps. – Proceedings, International Snow Science Workshop, Breckenridge, Colorado, 2016, pp. 132– 139.
18. Battisti L. Wind Turbines in Cold Climates: Icing Impacts and Mitigation Systems, Green Energy and Technology, Springer, 2015.
19. Parent O., Ilinca A. Anti-Icing and De-Icing Techniques for Wind Turbines: Critical review. – Cold Regions Science and Technology, 2011, 65(1), pp. 88–96, DOI:10.1016/j.coldregions.2010.01.005.
20. Homola M.C., et al. Effect of Atmospheric Temperature and Droplet Size Variation on Ice Accretion of Wind Turbine Blades. – Journal of Wind Engineering and Industrial Aerodynamics, 2010, 98(10), DOI:10.1016/j.jweia.2010.06.007.
2. Projected Costs of Generating Electricity 2020, IEA, Paris [Электрон. ресурс], URL: https://www.iea.org/reports/projected-costs-of-generating-electricity-2020 (дата обращения 8.12.2021).
3. Kazem H.A., et al. Optimum design and evaluation of hybrid solar/wind/diesel power system for Masirah Island. – Environment, Development and Sustainability, 2017, 19 (5), pp. 1761–1778, DOI:10.1007/s10668-016-9828-1.
4. Elistratov V.V., et al. The Application of Adapted Materials and Technologies to Create Energy Systems Based on Renewable Energy Sources under Harsh Climatic Condition. – Applied Solar Energy, 2018, 54(6), pp. 472–476, DOI:10.3103/S0003701X18060087.
5. Elistratov V., et al. Study of the Intelligent Control and Modes of the Arctic-Adopted Wind–Diesel Hybrid System. – Energies, 2021, 14, 4188, DOI:10.3390/en14144188.
6. Elistratov V.V., Denisov R.S. Energetic and Ecological Justification of RE-hybrid Systems for Vulnerable Ecosystems. – IOP Conf. Series: Earth and Environmental Science, 2021, 689, DOI:10.1088/1755-1315/689/1/012017.
7. Ansari M.M.T., Sangoden V. DMLHFLC (Dual Mode Linguistic Hedge Fuzzy Logic Controller) for an Isolated Wind-Diesel Hybrid Power System with BES (Battery Energy Storage) Unit. – Energy, 2010, 35(9), pp. 3827– 3837, DOI:10.1016/j.energy.2010.05.037.
8. Vachirasricirikul S., Ngamroo I., Kaitwanidvilai S. Coordinated SVC and AVR for Robust Voltage Control in a Hybrid Wind-Diesel System. – Energy Conversion and Management, 2010, 51, pp. 2383–2393, DOI:10.1016/j.enconman.2010.05.001.
9. Elistratov V.V., Bogun I.V., Kasina V.I. Optimization of Wind-Diesel Power Plants Parameters and Placement for Power Supply of Russia’s Northern Regions Consumers. – 16th Conference on Electrical Machines, Drives and Power Systems (ELMA), 2019, 8771647, DOI: 10.1109/ELMA.2019.8771647.
10. Shukur O.B., Lee M.H. Daily Wind Speed Forecasting Through Hybrid KF-ANN model based on ARIMA. – Renewable Energy, 2015, 76, pp. 637–647, DOI:10.1016/j.renene.2014.11.084.
11. Aasim, Singh S.N., Mohapatra A. Repeated Wavelet Transform Based ARIMA Model for Very Short-Term Wind Speed Forecasting. – Renewable Energy, 2019,136, pp. 758–768, DOI:10.1016/j.renene.2019.01.031.
12. Carpinone A., et al. Markov Chain Modeling for Very-Short-Term Wind Power Forecasting. Electric Power Systems Research 2015, 122, pp. 152–158, DOI:10.1016/j.epsr.2014.12.025.
13. D’Amico G., et al. Managing Wind Power Generation via Indexed Semi-Markov Model and Copula. – Energies, 2020, 13, 4246, DOI:10.3390/en13164246.
14. Marugán A.P., et al. A Survey of Artificial Neural Network In Wind Energy Systems. – Applied Energy, 2018, 228, pp. 1822–836, DOI:10.1016/j.apenergy.2018.07.084.
15. Santhosh M., Venkaiah C., Vinod Kumar D.M. Ensemble Empirical Mode Decomposition Based Adaptive Wavelet Neural Network Method for Wind Speed Prediction. – Energy Conversion and Management, 2018, 168, pp. 482– 493, DOI:10.1016/j.enconman.2018.04.099.
16. Huang C., et al. Short Term Wind Speed Predictions by Using the Grey Prediction Model Based Forecast Method. – IEEE Green Technologies Conference (IEEE-Green), 2011, DOI: 10.1109/GREEN.2011.5754856.
17. Gobiet A., et al. Operational Forecasting of Wet Snow Avalanche Activity: a Case Study for the Eastern European Alps. – Proceedings, International Snow Science Workshop, Breckenridge, Colorado, 2016, pp. 132– 139.
18. Battisti L. Wind Turbines in Cold Climates: Icing Impacts and Mitigation Systems, Green Energy and Technology, Springer, 2015.
19. Parent O., Ilinca A. Anti-Icing and De-Icing Techniques for Wind Turbines: Critical review. – Cold Regions Science and Technology, 2011, 65(1), pp. 88–96, DOI:10.1016/j.coldregions.2010.01.005.
20. Homola M.C., et al. Effect of Atmospheric Temperature and Droplet Size Variation on Ice Accretion of Wind Turbine Blades. – Journal of Wind Engineering and Industrial Aerodynamics, 2010, 98(10), DOI:10.1016/j.jweia.2010.06.007.
#
1. Elistratov V.V. Vozobnovlyaemaya energetika (Renewable Energy). SPb.: Izd-vo politekhn. un-ta, 2016, 424 p.
2. Projected Costs of Generating Electricity 2020, IEA, Paris [Electron. resource], URL: https://www.iea.org/reports/projected-costs-of-generating-electricity-2020 (Date of appeal 8.12.2021).
3. Kazem H.A., et al. Optimum design and evaluation of hybrid solar/wind/diesel power system for Masirah Island. – Environment, Development and Sustainability, 2017, 19 (5), pp. 1761–1778, DOI:10.1007/s10668-016-9828-1.
4. Elistratov V.V., et al. The Application of Adapted Materials and Technologies to Create Energy Systems Based on Renewable Energy Sources under Harsh Climatic Condition. – Applied Solar Energy, 2018, 54(6), pp. 472–476, DOI:10.3103/S0003701X18060087.
5. Elistratov V., et al. Study of the Intelligent Control and Modes of the Arctic-Adopted Wind–Diesel Hybrid System. – Energies, 2021, 14, 4188, DOI:10.3390/en14144188.
6. Elistratov V.V., Denisov R.S. Energetic and Ecological Justification of RE-hybrid Systems for Vulnerable Ecosystems. – IOP Conf. Series: Earth and Environmental Science, 2021, 689, DOI:10.1088/1755-1315/689/1/012017.
7. Ansari M.M.T., Sangoden V. DMLHFLC (Dual Mode Linguistic Hedge Fuzzy Logic Controller) for an Isolated Wind-Diesel Hybrid Power System with BES (Battery Energy Storage) Unit. – Energy, 2010, 35(9), pp. 3827– 3837, DOI:10.1016/j.energy.2010.05.037.
8. Vachirasricirikul S., Ngamroo I., Kaitwanidvilai S. Coordinated SVC and AVR for Robust Voltage Control in a Hybrid Wind-Diesel System. – Energy Conversion and Management, 2010, 51, pp. 2383–2393, DOI:10.1016/j.enconman.2010.05.001.
9. Elistratov V.V., Bogun I.V., Kasina V.I. Optimization of Wind-Diesel Power Plants Parameters and Placement for Power Supply of Russia’s Northern Regions Consumers. – 16th Conference on Electrical Machines, Drives and Power Systems (ELMA), 2019, 8771647, DOI: 10.1109/ELMA.2019.8771647.
10. Shukur O.B., Lee M.H. Daily Wind Speed Forecasting Through Hybrid KF-ANN model based on ARIMA. – Renewable Energy, 2015, 76, pp. 637–647, DOI:10.1016/j.renene.2014.11.084.
11. Aasim, Singh S.N., Mohapatra A. Repeated Wavelet Transform Based ARIMA Model for Very Short-Term Wind Speed Forecasting. – Renewable Energy, 2019,136, pp. 758–768, DOI:10.1016/j.renene.2019.01.031.
12. Carpinone A., et al. Markov Chain Modeling for Very-Short-Term Wind Power Forecasting. Electric Power Systems Research 2015, 122, pp. 152–158, DOI:10.1016/j.epsr.2014.12.025.
13. D’Amico G., et al. Managing Wind Power Generation via Indexed Semi-Markov Model and Copula. – Energies, 2020, 13, 4246, DOI:10.3390/en13164246.
14. Marugán A.P., et al. A Survey of Artificial Neural Network In Wind Energy Systems. – Applied Energy, 2018, 228, pp. 1822–836, DOI:10.1016/j.apenergy.2018.07.084.
15. Santhosh M., Venkaiah C., Vinod Kumar D.M. Ensemble Empirical Mode Decomposition Based Adaptive Wavelet Neural Network Method for Wind Speed Prediction. – Energy Conversion and Management, 2018, 168, pp. 482– 493, DOI:10.1016/j.enconman.2018.04.099.
16. Huang C., et al. Short Term Wind Speed Predictions by Using the Grey Prediction Model Based Forecast Method. – IEEE Green Technologies Conference (IEEE-Green), 2011, DOI: 10.1109/GREEN.2011.5754856.
17. Gobiet A., et al. Operational Forecasting of Wet Snow Avalanche Activity: a Case Study for the Eastern European Alps. – Proceedings, International Snow Science Workshop, Breckenridge, Colorado, 2016, pp. 132– 139.
18. Battisti L. Wind Turbines in Cold Climates: Icing Impacts and Mitigation Systems, Green Energy and Technology, Springer, 2015.
19. Parent O., Ilinca A. Anti-Icing and De-Icing Techniques for Wind Turbines: Critical review. – Cold Regions Science and Technology, 2011, 65(1), pp. 88–96, DOI:10.1016/j.coldregions.2010.01.005.
20. Homola M.C., et al. Effect of Atmospheric Temperature and Droplet Size Variation on Ice Accretion of Wind Turbine Blades. – Journal of Wind Engineering and Industrial Aerodynamics, 2010, 98(10), DOI:10.1016/j.jweia.2010.06.007.
Published
2021-12-08
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