Опыт применения автоматизированных систем накопления энергии на базе литий-ионных аккумуляторных батарей в автономных солнечно-дизельных электростанциях
Ключевые слова:
системы накопления электрической энергии, повышение энергоэффективности, возобновляемая энергетика, литий-ионные аккумуляторные батареи
Аннотация
Рынок систем накопления электроэнергии интенсивно развивается: совершенствуются технологии, приобретается опыт их практического применения. Эти системы позволяют по-новому решать многие проблемы управления нормальными и аварийными режимами энергосистем. Преобладающая доля выработки электроэнергии на электростанциях малой энергетики приходится на дизельные, газопоршневые и газотурбинные установки. При этом требования к накопителям по мощности и энергоёмкости в настоящее время достаточно невысоки и вполне реализуемы, что позволяет отработать алгоритмы и законы управления ими. По мере совершенствования технологии и неизбежного снижения себестоимости системы накопления будут востребованы и в «большой» энергетике. В статье описан опыт разработки и применения автоматизированных систем накопления энергии на базе литий-ионных аккумуляторных батарей в составе автономных гибридных солнечно-дизельных электростанций для повышения энергоэффективности последних. Показано, что многие актуальные задачи выбора состава оборудования, организации структуры, ведения режимов, устойчивости и надёжности энергосистем с помощью накопителей энергии могут быть решены более эффективно, чем традиционными способами. Рассмотрены варианты решения таких задач, как снижение пиковых нагрузок на генерирующее оборудование, управление потоком мощности, повышение качества электроэнергии (частота, напряжение), экономика проекта (затраты и прибыль), хранение энергии (автономность/резервирование).
Литература
1. ГОСТ 32144-2013. Электрическая энергия. Совместимость технических средств электромагнитная. Нормы качества электрической энергии в системах электроснабжения общего назначения. М.: Стандартинформ. 2014, 16 с.
2. Сайт ABB. Energy Storage Keeping Smart Grids in Balance, Zurich, Switzerland. [Электрон. ресурс], URL: http://www.abb.com (дата обращения 06.11.2021).
3. Liu C., et al. Virtual Power Plants for a Sustainable Urban Future. – Sustainable Cities and Society, 2020, 65(4), 102640, DOI: 10.1016/j.scs.2020.102640.
4. Fang F., et al. Uncertainties of Virtual Power Plant: Problems and Countermeasures. – Applied Energy, 2019, 239, pp. 454–470, DOI:10.1016/j.apenergy.2019.01.224.
5. Luo Z., Hong S., Ding Yu. A Data Mining-Driven Incentive-Based Demand Response Scheme for a Virtual Power Plant. Appl. Energy, 2019, 239, 549–559, DOI:10.1016/j.apenergy.2019.01.142.
6. Agamah S.U., Ekonomou L. Energy Storage System Scheduling for Peak Demand Reduction Using Evolutionary Combinatorial Optimisation. – Sustainable Energy Technologies and Assessment, 2017, 23, 73–82, DOI: 10.1016/J.SETA.2017.08.003.
7. Yang H., et al. Distributed Optimal Dispatch of Virtual Power Plant via Limited Communication. – IEEE Transactions on Power Systems, 2013, 28, 3511–3512, DOI:10.1109/TPWRS.2013.2242702.
8. Shah C., Wies R. Algorithms for Optimal Power Flow in Isolated Distribution Networks Using Different Battery Energy Storage Models. – IEEE PES Innovative Smart Grid Technologies Conference (ISGT), 2020, DOI: 10.1109/ISGT45199.2020.9087717.
9. Mohseni-Bonab S.M., et al. Voltage Security Constrained Stochastic Programming Model for Day-Ahead BESS Schedule in Co-Optimization of T&D Systems. – IEEE Transactions on Su-stainable Energy, 2020,vol. 11, No. 1, pp. 391–404, DOI: 10.1109/TSTE.2019.2892024.
10. Sun H., et al. Master–Slave-Splitting Based Distributed Global Power Flow Method for Integrated Transmission and Distribution Analysis. – IEEE Transactions on Smart Grid, 2015, vol. 6, No. 3, pp. 1484-1492, DOI: 10.1109/TSG.2014.2336810.
11. Behi B., et al. Cost–Benefit Analysis of a Virtual Power Plant Including Solar PV, Flow Battery, Heat Pump, and Demand Management: A Western Australian Case Study. – Energies 2020, 13(10), 2614, DOI:10.3390/en13102614.
12. Mansor M.A., et al. Construction and Performance Investigation of Three-Phase Solar PV and Battery Energy Storage System Integrated UPQC. IEEE Access 2020, 8, 103511–103538, DOI: DOI:10.1109/ACCESS.2020.2997056.
13. Joshua A.M., Vittal K.P. Transient Behavioural Modelling of Battery Energy Storage System Supporting Microgrid. – IEEE Int. Conf. on Power Electronics, Smart Grid and Renewable Energy, 2020, DOI: 10.1109/PESGRE45664.2020.9070389.
14. Hasan M.M., et al. A Multi-Objective Co-Design Optimization Framework for Grid-Connected Hybrid Battery Energy Storage Systems: Optimal Sizing and Selection of Technology. – Energies, 2022, 15 (15), 5355, DOI: 10.3390/en15155355.
15. Кувшинов В.В., Морозова Н.В., Кузнецов П.Н. Установки для солнечной энергетики. М.: Спутник +, 2017, 177 с.
16. Kumar R., Channi H.K. A PV-Biomass off-grid Hybrid Renewable Energy System (HRES) for Rural Electrification: Design, Optimization and Techno-Economic-Environmental Analysis. – Journal of Cleaner Production, 2022, vol. 349, 131347, DOI: 10.1016/j.jclepro.2022.131347
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1. GOST 32144-2013. Elektricheskaya energiya. Sovmestimost' tekhnicheskikh sredstv elektromagnitnaya. Normy kachestva elektricheskoy energii v sistemakh elektrosnabzheniya obshchego naznacheniya (Electric Energy. Electromagnetic Compatibility of Technical Equipment. Power Quality Limits in the Public Power Supply Systems). M.: Standartinform, 2014, 16 p.
2. ABB Website. Energy Storage Keeping Smart Grids in Balance, Zurich, Switzerland. [Electron. resource], URL: http://www.abb.com (Date of appeal 06.11.2021).
3. Liu C., et al. Virtual Power Plants for a Sustainable Urban Future. – Sustainable Cities and Society, 2020, 65(4), 102640, DOI:10.1016/ j.scs.2020.102640.
4. Fang F., et al. Uncertainties of Virtual Power Plant: Problems and Countermeasures. – Applied Energy, 2019, 239, pp. 454–470, DOI:10.1016/j.apenergy.2019.01.224.
5. Luo Z., Hong S., Ding Yu. A Data Mining-Driven Incentive-Based Demand Response Scheme for a Virtual Power Plant. Appl. Energy, 2019, 239, 549–559, DOI:10.1016/j.apenergy.2019.01.142.
6. Agamah S.U., Ekonomou L. Energy Storage System Scheduling for Peak Demand Reduction Using Evolutionary Combinatorial Optimisation. – Sustainable Energy Technologies and Assessment, 2017, 23, 73–82, DOI: 10.1016/J.SETA.2017.08.003.
7. Yang H., et al. Distributed Optimal Dispatch of Virtual Power Plant via Limited Communication. – IEEE Transactions on Power Systems, 2013, 28, 3511–3512, DOI:10.1109/TPWRS.2013.2242702.
8. Shah C., Wies R. Algorithms for Optimal Power Flow in Isolated Distribution Networks Using Different Battery Energy Storage Models. – IEEE PES Innovative Smart Grid Technologies Conference (ISGT), 2020, DOI: 10.1109/ISGT45199.2020.9087717.
9. Mohseni-Bonab S.M., et al. Voltage Security Constrained Stochastic Programming Model for Day-Ahead BESS Schedule in Co-Optimization of T&D Systems. – IEEE Transactions on Sustainable Energy, 2020,vol. 11, No. 1, pp. 391–404, DOI: 10.1109/TSTE.2019. 2892024.
10. Sun H., et al. Master–Slave-Splitting Based Distributed Global Power Flow Method for Integrated Transmission and Distribution Analysis. – IEEE Transactions on Smart Grid, 2015, vol. 6, No. 3, pp. 1484-1492, DOI: 10.1109/TSG.2014.2336810.
11. Behi B., et al. Cost–Benefit Analysis of a Virtual Power Plant Including Solar PV, Flow Battery, Heat Pump, and Demand Management: A Western Australian Case Study. – Energies 2020, 13(10), 2614, DOI:10.3390/en13102614.
12. Mansor M.A., et al. Construction and Performance Investigation of Three-Phase Solar PV and Battery Energy Storage System Integrated UPQC. IEEE Access 2020, 8, 103511–103538, DOI: DOI:10.1109/ACCESS.2020.2997056.
13. Joshua A.M., Vittal K.P. Transient Behavioural Modelling of Battery Energy Storage System Supporting Microgrid. – IEEE Int. Conf. on Power Electronics, Smart Grid and Renewable Energy, 2020, DOI: 10.1109/PESGRE45664.2020.9070389.
14. Hasan M.M., et al. A Multi-Objective Co-Design Optimization Framework for Grid-Connected Hybrid Battery Energy Storage Systems: Optimal Sizing and Selection of Technology. – Energies, 2022, 15 (15), 5355, DOI: 10.3390/en15155355.
15. Kuvshinov V.V., Morozova N.V., Kuznetsov P.N. Ustanovki dlya solnechnoy energetiki (Installations for Solar Energy). М.: Sputnik +, 2017, 177 p.
16. Kumar R., Channi H.K. A PV-Biomass off-grid Hybrid Renewable Energy System (HRES) for Rural Electrification: Design, Optimization and Techno-Economic-Environmental Analysis. – Journal of Cleaner Production, 2022, vol. 349, 131347, DOI: 10.1016/j.jclepro.2022.131347
2. Сайт ABB. Energy Storage Keeping Smart Grids in Balance, Zurich, Switzerland. [Электрон. ресурс], URL: http://www.abb.com (дата обращения 06.11.2021).
3. Liu C., et al. Virtual Power Plants for a Sustainable Urban Future. – Sustainable Cities and Society, 2020, 65(4), 102640, DOI: 10.1016/j.scs.2020.102640.
4. Fang F., et al. Uncertainties of Virtual Power Plant: Problems and Countermeasures. – Applied Energy, 2019, 239, pp. 454–470, DOI:10.1016/j.apenergy.2019.01.224.
5. Luo Z., Hong S., Ding Yu. A Data Mining-Driven Incentive-Based Demand Response Scheme for a Virtual Power Plant. Appl. Energy, 2019, 239, 549–559, DOI:10.1016/j.apenergy.2019.01.142.
6. Agamah S.U., Ekonomou L. Energy Storage System Scheduling for Peak Demand Reduction Using Evolutionary Combinatorial Optimisation. – Sustainable Energy Technologies and Assessment, 2017, 23, 73–82, DOI: 10.1016/J.SETA.2017.08.003.
7. Yang H., et al. Distributed Optimal Dispatch of Virtual Power Plant via Limited Communication. – IEEE Transactions on Power Systems, 2013, 28, 3511–3512, DOI:10.1109/TPWRS.2013.2242702.
8. Shah C., Wies R. Algorithms for Optimal Power Flow in Isolated Distribution Networks Using Different Battery Energy Storage Models. – IEEE PES Innovative Smart Grid Technologies Conference (ISGT), 2020, DOI: 10.1109/ISGT45199.2020.9087717.
9. Mohseni-Bonab S.M., et al. Voltage Security Constrained Stochastic Programming Model for Day-Ahead BESS Schedule in Co-Optimization of T&D Systems. – IEEE Transactions on Su-stainable Energy, 2020,vol. 11, No. 1, pp. 391–404, DOI: 10.1109/TSTE.2019.2892024.
10. Sun H., et al. Master–Slave-Splitting Based Distributed Global Power Flow Method for Integrated Transmission and Distribution Analysis. – IEEE Transactions on Smart Grid, 2015, vol. 6, No. 3, pp. 1484-1492, DOI: 10.1109/TSG.2014.2336810.
11. Behi B., et al. Cost–Benefit Analysis of a Virtual Power Plant Including Solar PV, Flow Battery, Heat Pump, and Demand Management: A Western Australian Case Study. – Energies 2020, 13(10), 2614, DOI:10.3390/en13102614.
12. Mansor M.A., et al. Construction and Performance Investigation of Three-Phase Solar PV and Battery Energy Storage System Integrated UPQC. IEEE Access 2020, 8, 103511–103538, DOI: DOI:10.1109/ACCESS.2020.2997056.
13. Joshua A.M., Vittal K.P. Transient Behavioural Modelling of Battery Energy Storage System Supporting Microgrid. – IEEE Int. Conf. on Power Electronics, Smart Grid and Renewable Energy, 2020, DOI: 10.1109/PESGRE45664.2020.9070389.
14. Hasan M.M., et al. A Multi-Objective Co-Design Optimization Framework for Grid-Connected Hybrid Battery Energy Storage Systems: Optimal Sizing and Selection of Technology. – Energies, 2022, 15 (15), 5355, DOI: 10.3390/en15155355.
15. Кувшинов В.В., Морозова Н.В., Кузнецов П.Н. Установки для солнечной энергетики. М.: Спутник +, 2017, 177 с.
16. Kumar R., Channi H.K. A PV-Biomass off-grid Hybrid Renewable Energy System (HRES) for Rural Electrification: Design, Optimization and Techno-Economic-Environmental Analysis. – Journal of Cleaner Production, 2022, vol. 349, 131347, DOI: 10.1016/j.jclepro.2022.131347
#
1. GOST 32144-2013. Elektricheskaya energiya. Sovmestimost' tekhnicheskikh sredstv elektromagnitnaya. Normy kachestva elektricheskoy energii v sistemakh elektrosnabzheniya obshchego naznacheniya (Electric Energy. Electromagnetic Compatibility of Technical Equipment. Power Quality Limits in the Public Power Supply Systems). M.: Standartinform, 2014, 16 p.
2. ABB Website. Energy Storage Keeping Smart Grids in Balance, Zurich, Switzerland. [Electron. resource], URL: http://www.abb.com (Date of appeal 06.11.2021).
3. Liu C., et al. Virtual Power Plants for a Sustainable Urban Future. – Sustainable Cities and Society, 2020, 65(4), 102640, DOI:10.1016/ j.scs.2020.102640.
4. Fang F., et al. Uncertainties of Virtual Power Plant: Problems and Countermeasures. – Applied Energy, 2019, 239, pp. 454–470, DOI:10.1016/j.apenergy.2019.01.224.
5. Luo Z., Hong S., Ding Yu. A Data Mining-Driven Incentive-Based Demand Response Scheme for a Virtual Power Plant. Appl. Energy, 2019, 239, 549–559, DOI:10.1016/j.apenergy.2019.01.142.
6. Agamah S.U., Ekonomou L. Energy Storage System Scheduling for Peak Demand Reduction Using Evolutionary Combinatorial Optimisation. – Sustainable Energy Technologies and Assessment, 2017, 23, 73–82, DOI: 10.1016/J.SETA.2017.08.003.
7. Yang H., et al. Distributed Optimal Dispatch of Virtual Power Plant via Limited Communication. – IEEE Transactions on Power Systems, 2013, 28, 3511–3512, DOI:10.1109/TPWRS.2013.2242702.
8. Shah C., Wies R. Algorithms for Optimal Power Flow in Isolated Distribution Networks Using Different Battery Energy Storage Models. – IEEE PES Innovative Smart Grid Technologies Conference (ISGT), 2020, DOI: 10.1109/ISGT45199.2020.9087717.
9. Mohseni-Bonab S.M., et al. Voltage Security Constrained Stochastic Programming Model for Day-Ahead BESS Schedule in Co-Optimization of T&D Systems. – IEEE Transactions on Sustainable Energy, 2020,vol. 11, No. 1, pp. 391–404, DOI: 10.1109/TSTE.2019. 2892024.
10. Sun H., et al. Master–Slave-Splitting Based Distributed Global Power Flow Method for Integrated Transmission and Distribution Analysis. – IEEE Transactions on Smart Grid, 2015, vol. 6, No. 3, pp. 1484-1492, DOI: 10.1109/TSG.2014.2336810.
11. Behi B., et al. Cost–Benefit Analysis of a Virtual Power Plant Including Solar PV, Flow Battery, Heat Pump, and Demand Management: A Western Australian Case Study. – Energies 2020, 13(10), 2614, DOI:10.3390/en13102614.
12. Mansor M.A., et al. Construction and Performance Investigation of Three-Phase Solar PV and Battery Energy Storage System Integrated UPQC. IEEE Access 2020, 8, 103511–103538, DOI: DOI:10.1109/ACCESS.2020.2997056.
13. Joshua A.M., Vittal K.P. Transient Behavioural Modelling of Battery Energy Storage System Supporting Microgrid. – IEEE Int. Conf. on Power Electronics, Smart Grid and Renewable Energy, 2020, DOI: 10.1109/PESGRE45664.2020.9070389.
14. Hasan M.M., et al. A Multi-Objective Co-Design Optimization Framework for Grid-Connected Hybrid Battery Energy Storage Systems: Optimal Sizing and Selection of Technology. – Energies, 2022, 15 (15), 5355, DOI: 10.3390/en15155355.
15. Kuvshinov V.V., Morozova N.V., Kuznetsov P.N. Ustanovki dlya solnechnoy energetiki (Installations for Solar Energy). М.: Sputnik +, 2017, 177 p.
16. Kumar R., Channi H.K. A PV-Biomass off-grid Hybrid Renewable Energy System (HRES) for Rural Electrification: Design, Optimization and Techno-Economic-Environmental Analysis. – Journal of Cleaner Production, 2022, vol. 349, 131347, DOI: 10.1016/j.jclepro.2022.131347
