Formulating and Solving an Optimization Problem with Heterogeneous Optimality Criteria in Evaluating the Dynamic System Performance
DOI:
https://doi.org/10.24160/1993-6982-2021-2-88-97Keywords:
multi-agent system, MicroGrid, MicroGrid efficiency, fuzzy setsAbstract
An approach to formulating and solving the problem of comprehensively evaluating the dynamic system performance is proposed. A MicroGrid represented in a multi-agent system (MAS) form is taken as an example. A unified definition of agents applied to the class of dynamic systems formalized in the form of continuous, discrete and discrete-event models is introduced. The performance efficiency of both the MicroGrid and its individual components (agents) is assessed. The developed flowchart for MicroGrid performance assessment serves as a basis for optimizing the MicroGrid performance indicators in the online mode. By using the proposed solution, it becomes possible to formalize the integration of heterogeneous objective functions into unified criteria by certain types and also taking into account the performance assessments of individual components in the interconnected system. Technical, economic and environmental criteria are considered as standard performance criteria. To obtain a generalized solution with taking into account the heterogeneity of the considered efficiency criteria, a fuzzy model based on the fuzzy sets theory is proposed as a tool for convolution of these criteria. The flowchart of the algorithm for MicroGrid performance assessment is developed taking as an example the design of a hybrid-generating and environmentally safe power supply system for the Arctic enclave with a specified configuration.
References
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11. Nafisi H., Agha M.M., Abyaneh H.A., Abedi M. Two-stage Optimization Method for Energy Loss Minimization in MicroGrid Based on Smart Power Management Scheme of Phevs // IEEE Trans. Smart Grid. 2016. V. 7. No. 3. Pp. 1268—1276.
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13. Dehghanpour K., Nehrir H. Real-time Multiobjective MicroGrid Power Management Using Distributed Optimization in an Agent-Based Bargaining Framework // IEEE Trans. Smart Grid. 2017. V. 9. No. 6. Pp. 6318—6327.
14. Peigen Tian, Xi Xiao, Kui Wang, Ruoxing Ding. A Hierarchical Energy Management System Based on Hierarchical Optimization for MicroGrid Community Economic Operation // IEEE Trans. Smart Grid. 2016. V. 7. Iss. 5. Pp. 2230—2241.
15. Постановление Правительства РФ № 1401 от 19 декабря 2016 г. О комплексном определении показателей технико-экономического состояния объектов электроэнергетики, в том числе показателей физического износа и энергетической эффективности объектов электросетевого хозяйства, и об осуществлении мониторинга таких показателей.
16. ГОСТ 32144—2013. Электрическая энергия. Совместимость технических средств электромагнитная. Нормы качества электрической энергии в системах электроснабжения общего назначения.
17. Абд Эльрахим А.К., Шихин В.А. Оценка показателей эффективности функционирования энергообъектов в мультиагентной микроэнергосистеме // Автоматизация. Современные технологии. 2019. Т. 73. № 10. С. 449—460.
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19. EPA Air Pollution Control Cost Manual. North Carolina: Office of Air Quality Planning and Standards Research Triangle Park, 2002.
20. Erdogdu E. The Impact of Power Market Reforms on Electricity Price-Cost Margins and Cross-Subsidy Levels: a Cross-Country Panel Data Analysis // Energy Policy. 2011. V. 39(3). Pp. 1—35.
---
Для цитирования: Абд Эльрахим А.К., Шихин В.А. Формулировка и решение оптимизационной задачи с разнородными критериями оптимальности при оценивании эффективности функционирования динамической системы // Вестник МЭИ. 2021. № 2. С. 88—97. DOI: 10.24160/1993-6982-2021-2-88-97.
#
1. Leitao P., Marik V., Vrba P. Past Present and Future of Industrial Agent Applications. IEEE Trans. Ind. Information. 2013;9;4:2360—2372.
2. SmartGrids SRA 2035 — Strategic Research Agenda: European Technology Platform SmartGrids. European Commission. Tech. Rep. Brusseles, 2012:20—27.
3. Rohbogner G., Hahnel Ulf J.J., Benoit P., Fey S. Multi-agent Systems’ Asset for Smart Grid Applications. Computer Sci. and Information Syst. 2013;10. Iss. 4:1799—1822.
4. McArthur S. e. a. Multi-agent Systems for Power Engineering Applications. P. 1: Concepts, Approaches and Technical Challenges. IEEE Trans. Power Syst. 2007;22;4:1743—1752.
5. Kantamneni A., Brown L., Parker G., Weaver W. Survey of Multi-agent Systems for MicroGrid Control. Eng. Appl. Artificial Intelligence. 2015;45:192—203.
6. Abd Elraheem A.K., Shikhin V.A., Kouzalis A. Optimization Problem Statement for Power Generation Management and Control in Multi-Agent Microid. Proc. III Intern. Conf. Control Techn. Syst. 2019:176—179.
7. Marnay C. e. a. MicroGrid Evolution Roadmap. Proc. Intern. Symp. Smart Electric Distribution Syst. and Technol. 2015:139—144.
8. Guerrero J.M. e. a. Shipboard MicroGrids: Maritime Islanded Power Systems Technologies. Proc. Intern. Exhibition and Conf. Power Electronics, Intelligent Motion, Renewable Energy and Energy Management. Shanghai, 2016:1—8.
9. Wang Y., Huang Y., Wang Y., Li F., Zhang Y., Tian C. Operation Optimization in a Smart MicroGrid in the Presence of Distributed Generation and Demand Response. Sustainability. 2018;10:847—872.
10. Dulau L.I., Bica, D. Optimization of Generation Cost in a MicroGrid. Procedia Manufacturing. 2018;22:703—708.
11. Nafisi H., Agha M.M., Abyaneh H.A., Abedi M. Two-stage Optimization Method for Energy Loss Minimization in MicroGrid Based on Smart Power Management Scheme of Phevs. IEEE Trans. Smart Grid. 2016;7;3:1268—1276.
12. Minchala-Avila L.I., Garza-Castanon L.E., Vargas-Martınez A., Zhangc Y. Review of Optimal Control Techniques Applied to the Energy Management and Control of MicroGrids. Procedia Computer Sci. 2015;52:780—787.
13. Dehghanpour K., Nehrir H. Real-time Multiobjective MicroGrid Power Management Using Distributed Optimization in an Agent-Based Bargaining Framework. IEEE Trans. Smart Grid. 2017;9;6:6318—6327.
14. Peigen Tian, Xi Xiao, Kui Wang, Ruoxing Ding. A Hierarchical Energy Management System Based on Hierarchical Optimization for MicroGrid Community Economic Operation. IEEE Trans. Smart Grid. 2016;7;5:2230—2241.
15. Postanovlenie Pravitel'stva RF № 1401 ot 19 Dekabrya 2016 g. O Kompleksnom Opredelenii Pokazateley Tekhniko-ekonomicheskogo Sostoyaniya Ob′ektov Elektroenergetiki, v tom Chisle Pokazateley Fizicheskogo Iznosa i Energeticheskoy Effektivnosti Ob′ektov Elektrosetevogo Khozyaystva, i ob Osushchestvlenii Monitoringa Takikh Pokazateley. (in Russian).
16. GOST 32144—2013. Elektricheskaya Energiya. Sovmestimost' Tekhnicheskikh Sredstv Elektromagnitnaya. Normy Kachestva Elektricheskoy Energii v Sistemakh Elektrosnabzheniya Obshchego Naznacheniya. (in Russian).
17. Abd El'rakhim A.K., Shikhin V.A. Otsenka Pokazateley Effektivnosti Funktsionirovaniya Energoob′ektov v Mul'tiagentnoy Mikroenergosisteme. Avtomatizatsiya. Sovremennye Tekhnologii. 2019;73;10:449—460. (in Russian).
18. Bertling L., Wallnerstrom C.J. Exercises on Reliability Assessment of Electric Power Systems. Stockholm: Royal Institute of Technology, School of Electrical Engineering, 2007.
19. EPA Air Pollution Control Cost Manual. North Carolina: Office of Air Quality Planning and Standards Research Triangle Park, 2002.
20. Erdogdu E. The Impact of Power Market Reforms on Electricity Price-Cost Margins and Cross-Subsidy Levels: a Cross-Country Panel Data Analysis. Energy Policy. 2011;39(3):1—35.
---
For citation: Abd Elraheem A.K., Shikhin V.A. Formulating and Solving an Optimization Problem with Heterogeneous Optimality Criteria in Evaluating the Dynamic System Performance. Bulletin of MPEI. 2021;2:88—97. (in Russian). DOI: 10.24160/1993-6982-2021-2-88-97.
2. SmartGrids SRA 2035 — Strategic Research Agenda: European Technology Platform SmartGrids. European Commission. Tech. Rep. Brusseles, 2012. Pp. 20—27.
3. Rohbogner G., Hahnel Ulf J.J., Benoit P., Fey S. Multi-agent Systems’ Asset for Smart Grid Applications // Computer Sci. and Information Syst. 2013. V. 10. Iss. 4. Pp. 1799—1822.
4. McArthur S. e. a. Multi-agent Systems for Power Engineering Applications. P. 1: Concepts, Approaches and Technical Challenges // IEEE Trans. Power Syst. 2007. V. 22. No. 4. Pp. 1743—1752.
5. Kantamneni A., Brown L., Parker G., Weaver W. Survey of Multi-agent Systems for MicroGrid Control // Eng. Appl. Artificial Intelligence. 2015. No. 45. Pp. 192—203.
6. Abd Elraheem A.K., Shikhin V.A., Kouzalis A. Optimization Problem Statement for Power Generation Management and Control in Multi-Agent Microid // Proc. III Intern. Conf. Control Techn. Syst. 2019. Pp. 176—179.
7. Marnay C. e. a. MicroGrid Evolution Roadmap // Proc. Intern. Symp. Smart Electric Distribution Syst. and Technol. 2015. Pp. 139—144.
8. Guerrero J.M. e. a. Shipboard MicroGrids: Maritime Islanded Power Systems Technologies // Proc. Intern. Exhibition and Conf. Power Electronics, Intelligent Motion, Renewable Energy and Energy Management. Shanghai, 2016. Pp. 1—8.
9. Wang Y., Huang Y., Wang Y., Li F., Zhang Y., Tian C. Operation Optimization in a Smart MicroGrid in the Presence of Distributed Generation and Demand Response // Sustainability. 2018. No. 10. Pp. 847—872.
10. Dulau L.I., Bica, D. Optimization of Generation Cost in a MicroGrid // Procedia Manufacturing. 2018. No. 22. Pp. 703—708.
11. Nafisi H., Agha M.M., Abyaneh H.A., Abedi M. Two-stage Optimization Method for Energy Loss Minimization in MicroGrid Based on Smart Power Management Scheme of Phevs // IEEE Trans. Smart Grid. 2016. V. 7. No. 3. Pp. 1268—1276.
12. Minchala-Avila L.I., Garza-Castanon L.E., Vargas-Martınez A., Zhangc Y. Review of Optimal Control Techniques Applied to the Energy Management and Control of MicroGrids // Procedia Computer Sci. 2015. V. 52. Pp. 780—787.
13. Dehghanpour K., Nehrir H. Real-time Multiobjective MicroGrid Power Management Using Distributed Optimization in an Agent-Based Bargaining Framework // IEEE Trans. Smart Grid. 2017. V. 9. No. 6. Pp. 6318—6327.
14. Peigen Tian, Xi Xiao, Kui Wang, Ruoxing Ding. A Hierarchical Energy Management System Based on Hierarchical Optimization for MicroGrid Community Economic Operation // IEEE Trans. Smart Grid. 2016. V. 7. Iss. 5. Pp. 2230—2241.
15. Постановление Правительства РФ № 1401 от 19 декабря 2016 г. О комплексном определении показателей технико-экономического состояния объектов электроэнергетики, в том числе показателей физического износа и энергетической эффективности объектов электросетевого хозяйства, и об осуществлении мониторинга таких показателей.
16. ГОСТ 32144—2013. Электрическая энергия. Совместимость технических средств электромагнитная. Нормы качества электрической энергии в системах электроснабжения общего назначения.
17. Абд Эльрахим А.К., Шихин В.А. Оценка показателей эффективности функционирования энергообъектов в мультиагентной микроэнергосистеме // Автоматизация. Современные технологии. 2019. Т. 73. № 10. С. 449—460.
18. Bertling L., Wallnerstrom C.J. Exercises on Reliability Assessment of Electric Power Systems. Stockholm: Royal Institute of Technology, School of Electrical Engineering, 2007.
19. EPA Air Pollution Control Cost Manual. North Carolina: Office of Air Quality Planning and Standards Research Triangle Park, 2002.
20. Erdogdu E. The Impact of Power Market Reforms on Electricity Price-Cost Margins and Cross-Subsidy Levels: a Cross-Country Panel Data Analysis // Energy Policy. 2011. V. 39(3). Pp. 1—35.
---
Для цитирования: Абд Эльрахим А.К., Шихин В.А. Формулировка и решение оптимизационной задачи с разнородными критериями оптимальности при оценивании эффективности функционирования динамической системы // Вестник МЭИ. 2021. № 2. С. 88—97. DOI: 10.24160/1993-6982-2021-2-88-97.
#
1. Leitao P., Marik V., Vrba P. Past Present and Future of Industrial Agent Applications. IEEE Trans. Ind. Information. 2013;9;4:2360—2372.
2. SmartGrids SRA 2035 — Strategic Research Agenda: European Technology Platform SmartGrids. European Commission. Tech. Rep. Brusseles, 2012:20—27.
3. Rohbogner G., Hahnel Ulf J.J., Benoit P., Fey S. Multi-agent Systems’ Asset for Smart Grid Applications. Computer Sci. and Information Syst. 2013;10. Iss. 4:1799—1822.
4. McArthur S. e. a. Multi-agent Systems for Power Engineering Applications. P. 1: Concepts, Approaches and Technical Challenges. IEEE Trans. Power Syst. 2007;22;4:1743—1752.
5. Kantamneni A., Brown L., Parker G., Weaver W. Survey of Multi-agent Systems for MicroGrid Control. Eng. Appl. Artificial Intelligence. 2015;45:192—203.
6. Abd Elraheem A.K., Shikhin V.A., Kouzalis A. Optimization Problem Statement for Power Generation Management and Control in Multi-Agent Microid. Proc. III Intern. Conf. Control Techn. Syst. 2019:176—179.
7. Marnay C. e. a. MicroGrid Evolution Roadmap. Proc. Intern. Symp. Smart Electric Distribution Syst. and Technol. 2015:139—144.
8. Guerrero J.M. e. a. Shipboard MicroGrids: Maritime Islanded Power Systems Technologies. Proc. Intern. Exhibition and Conf. Power Electronics, Intelligent Motion, Renewable Energy and Energy Management. Shanghai, 2016:1—8.
9. Wang Y., Huang Y., Wang Y., Li F., Zhang Y., Tian C. Operation Optimization in a Smart MicroGrid in the Presence of Distributed Generation and Demand Response. Sustainability. 2018;10:847—872.
10. Dulau L.I., Bica, D. Optimization of Generation Cost in a MicroGrid. Procedia Manufacturing. 2018;22:703—708.
11. Nafisi H., Agha M.M., Abyaneh H.A., Abedi M. Two-stage Optimization Method for Energy Loss Minimization in MicroGrid Based on Smart Power Management Scheme of Phevs. IEEE Trans. Smart Grid. 2016;7;3:1268—1276.
12. Minchala-Avila L.I., Garza-Castanon L.E., Vargas-Martınez A., Zhangc Y. Review of Optimal Control Techniques Applied to the Energy Management and Control of MicroGrids. Procedia Computer Sci. 2015;52:780—787.
13. Dehghanpour K., Nehrir H. Real-time Multiobjective MicroGrid Power Management Using Distributed Optimization in an Agent-Based Bargaining Framework. IEEE Trans. Smart Grid. 2017;9;6:6318—6327.
14. Peigen Tian, Xi Xiao, Kui Wang, Ruoxing Ding. A Hierarchical Energy Management System Based on Hierarchical Optimization for MicroGrid Community Economic Operation. IEEE Trans. Smart Grid. 2016;7;5:2230—2241.
15. Postanovlenie Pravitel'stva RF № 1401 ot 19 Dekabrya 2016 g. O Kompleksnom Opredelenii Pokazateley Tekhniko-ekonomicheskogo Sostoyaniya Ob′ektov Elektroenergetiki, v tom Chisle Pokazateley Fizicheskogo Iznosa i Energeticheskoy Effektivnosti Ob′ektov Elektrosetevogo Khozyaystva, i ob Osushchestvlenii Monitoringa Takikh Pokazateley. (in Russian).
16. GOST 32144—2013. Elektricheskaya Energiya. Sovmestimost' Tekhnicheskikh Sredstv Elektromagnitnaya. Normy Kachestva Elektricheskoy Energii v Sistemakh Elektrosnabzheniya Obshchego Naznacheniya. (in Russian).
17. Abd El'rakhim A.K., Shikhin V.A. Otsenka Pokazateley Effektivnosti Funktsionirovaniya Energoob′ektov v Mul'tiagentnoy Mikroenergosisteme. Avtomatizatsiya. Sovremennye Tekhnologii. 2019;73;10:449—460. (in Russian).
18. Bertling L., Wallnerstrom C.J. Exercises on Reliability Assessment of Electric Power Systems. Stockholm: Royal Institute of Technology, School of Electrical Engineering, 2007.
19. EPA Air Pollution Control Cost Manual. North Carolina: Office of Air Quality Planning and Standards Research Triangle Park, 2002.
20. Erdogdu E. The Impact of Power Market Reforms on Electricity Price-Cost Margins and Cross-Subsidy Levels: a Cross-Country Panel Data Analysis. Energy Policy. 2011;39(3):1—35.
---
For citation: Abd Elraheem A.K., Shikhin V.A. Formulating and Solving an Optimization Problem with Heterogeneous Optimality Criteria in Evaluating the Dynamic System Performance. Bulletin of MPEI. 2021;2:88—97. (in Russian). DOI: 10.24160/1993-6982-2021-2-88-97.
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Published
2020-10-21
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Section
System Analysis, Management and Information Processing (05.13.01)
