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Linearisierung der Prozesse Kraftwerkseinsatzplanung und Redispatch

Linearization of the unit commitment and redispatch problem
Christian Klabunde and Martin Wolter

Zusammenfassung

Die Optimierung von Engpassmanagementmaßnahmen ist ein bedeutender Forschungsschwerpunkt in der elektrischen Energieversorgung. Hierfür haben sich unterschiedlichste Methoden etabliert. Diese Methoden verwenden sowohl klassische als auch heuristische Optimierungsalgorithmen, um das Engpassmanagementproblem zu lösen. Der Fokus dieser Veröffentlichung liegt auf der Darstellung aller Schritte, die zur Linearisierung des Engpassmanagementproblems notwendig sind. Darauf aufbauend wird ein gemischt ganzzahliges lineares Optimierungsproblem aufgebaut, das sowohl die Kraftwerkseinsatzplanung als auch das Engpassmanagement umfasst. Die entwickelten Optimierungsalgorithmen werden auf ein Testszenario basierend auf dem IEEE 24 Knoten Netz angewandt. Mit Hilfe des vorgestellten Algorithmus ist es möglich, das Netz engpassfrei zu betreiben. Zum Ausgleich des Linearisierungsfehlers muss der Algorithmus auf das Beispielnetz dreimal angewandt werden.

Abstract

Optimizing congestion management measures is an important field of research in electric energy supply. Therefore, different methods are established. These methods are using classical as well as heuristic optimization algorithms to solve the congestion management problem. This contribution focuses on describing all steps that are necessary to linearize the congestion management problem. Based on this, mixed integer linear optimization algorithms are developed to solve the unit commitment and the congestion management process. The developed algorithms are applied on a test scenario based on IEEE 24 bus system. The developed algorithm ensures a network operation without contingencies. To compensate the linearization error, the optimization algorithm is applied three times on the given test system.

Literatur

1. Bundesnetzagentur für Elektrizität, Gas, Telekommunikation, Post und Eisenbahnen, „Monitoringbericht 2019“.Search in Google Scholar

2. S. Surender Reddy, “Multi-objective based congestion management using generation rescheduling and load shedding”, IEEE Transactions on Power Systems, pp. 1–12, 2016.10.1109/TPWRS.2016.2569603Search in Google Scholar

3. N. Alguacil and A. J. Conejo, “Multiperiod optimal power flow using Benders decomposition”, IEEE Transactions on Power Systems, vol. 15(1), Feb. 2000.10.1109/59.852121Search in Google Scholar

4. J. Martinez-Crespo, J. Usaola and J. L. Fernandez, “Optimal security-constrained power scheduling by Benders decomposition”, Electric Power Systems Research, vol. 77, 2007.10.1016/j.epsr.2006.06.009Search in Google Scholar

5. Y. Fu, M. Shahidehpour and Z. Li, “AC contingency dispatch based on security-constrained unit commitment”, IEEE Transactions on Power Systems, vol. 21(2), May 2006.10.1109/TPWRS.2006.873407Search in Google Scholar

6. S. Cvijic and J. Xiong, “Security constrained unit commitment and economic dispatch through Benders decomposition: a comparative study”, in: IEEE Power and Energy Society General Meeting, 2011.10.1109/PES.2011.6039643Search in Google Scholar

7. D. Singh and K. S. Verma, “GA-based congestion management in deregulated power system using Facts devices”, IEEE Transactions, pp. 1–6, 2012.10.1109/ICUEPES.2011.6497716Search in Google Scholar

8. S. Sivakumar, “Congestion management in deregulated power system by rescheduling of generators using Genetic Algorithm”, in: International Conference on Power, Signals, Controls and Computation (EPSCICON), pp. 1–5, 2014.10.1109/EPSCICON.2014.6887495Search in Google Scholar

9. S. Pal, S. Sen and S. Sengupta, “Power network reconfiguration for congestion management and loss minimization using Genetic Algorithm”, in: Michael Faraday IET International Summit: MFIIS-2015, Kolkata, India, pp. 291–296, 2015.10.1049/cp.2015.1646Search in Google Scholar

10. R. Rajesh, A. Mutharasan, T. Rameshkumar and B. Senthilkumaran, “Congestion management in deregulated environment using Generation Rescheduling with an intelligent approach”, International Journal of Applied Engineering Research, vol. 10(20), pp. 41665–41668, 2015.Search in Google Scholar

11. S. M. H. Nabavi, A. Kazemi and M. A. S. Masoum, “Congestion management using Genetic Algorithm in deregulated power environments”, International Journal of Computer Applications, vol. 18(2), pp. 19–23, 2011.10.5120/2257-2894Search in Google Scholar

12. K. B. Ravindrakumar and S. Chandramohan, “NSGA II based congestion management in deregulated power systems”, Middle-East Journal of Scientific Research, vol. 24(4), pp. 1188-1193, 2016.Search in Google Scholar

13. H. Mahala and Y. Kumar, “Active & reactive power rescheduling for congestion management using new PSO strategy”, in: IEEE Students’ Conference on Electrical, Electronics and Computer Science (SCEECS), pp. 1–4, 2016.10.1109/SCEECS.2016.7509301Search in Google Scholar

14. Adhip and D. Thukaram, “Congestion management based on virtual real power flows”, in: Biennial International Conference on Power and Energy Systems: Towards Sustainable Energy (PESTSE), pp. 1–5, 2016.10.1109/PESTSE.2016.7516419Search in Google Scholar

15. S. Chanda and A. De, “Application of Particle Swarm Optimization for relieving congestion in deregulated power system”, IEEE Transactions, pp. 837–840, 2011.10.1109/RAICS.2011.6069427Search in Google Scholar

16. N. Kinhekar, N. P. Padhy and H. O. Gupta, “Particle Swarm Optimization based Demand Response for residential consumers”, IEEE Transactions, pp. 1–5, 2015.10.1109/PESGM.2015.7286466Search in Google Scholar

17. S. Thangalakshmi and P. Valsalal, “Congestion management in restructured power systems with economic and technical considerations”, Asian Journal of Information Technology, vol. 15(12), pp. 2079–2086, 2016.Search in Google Scholar

18. C. N. Raja Kumari and M. Anitha, “Re-dispatch approach for congestion relief in deregulated power systems”, Journal of Engineering Trends and Technology (IJETT), vol. 4(5), pp. 1776–1780, 2013.Search in Google Scholar

19. G. Kumaravelan, N. Chidambararaj and K. Chitra, “Optimal active power rescheduling of generators for congestion management using new definition of sensitivity”, International Journal of Emerging Technology in Computer Science & Electronic (IJETCSE), vol. 13(1), pp. 111–115, 2015.Search in Google Scholar

20. K. Van den Bergh, J. Boury and E. Delarue, “The Flow-Based Market Couling in Central Western Europe: concepts and definitions”, TME Working Paper – Energy and Environment, KU Leuven, 2015.10.1016/j.tej.2015.12.004Search in Google Scholar

21. Statista, “Handelsvolumen am Spot- und Terminmarkt (EPEX SPOT und EEX) für Strom in den Jahren 2002 bis 2019“, URL: https://de.statista.com/statistik/daten/studie/12486/umfrage/entwicklung-der-eex-handelsvolumina/, abgerufen am: 22.12.2020.Search in Google Scholar

22. M. Z. Djurovic, A. Milacic and M. Krsulja, “A simplified model of quadratic cost function for thermal generators”, in: 23rd International DAAAM Symposium, 2012.10.2507/23rd.daaam.proceedings.006Search in Google Scholar

23. V. S. Aragon, S. C. Esquivel and C. A. Coello Coello, “An immune algorithm with power redistribution for solving economic dispatch problems”, Information Sciences, vol. 295, 2015.10.1016/j.ins.2014.10.026Search in Google Scholar

24. S. K. Gupta and P. Chawala, “Economic load dispatch in thermal power plant considering additional constraints using curve fitting and ANN”, Review of Energy Technologies and Policy Research, 2015.10.18488/journal.77/2015.2.1/77.1.16.28Search in Google Scholar

25. S. Sayah and A. Hamouda, “Efficient method for estimation of smooth and nonsmooth fuel cost curves for thermal power plants”, International Transactions on Electric Energy Systems, 2018.10.1002/etep.2498Search in Google Scholar

26. B. S. Wahyuda and A. Rusdiansyah, “Cost analysis of an electricity supply chain using modification of price based dynamic economic dispatch in wheeling transaction scheme”, in: International Conference on Industrial and System Engineering, 2017.10.1088/1757-899X/337/1/012009Search in Google Scholar

27. P. N. Biskas, C. G. Baslis, C. K. Simoglou and A. G. Bakirtzis, “Coordination of day-ahead scheduling with a stochastic weekly unit commitment for the efficient scheduling of slow-start thermal units”, in: IREP Symposium, 2010.10.1109/IREP.2010.5563262Search in Google Scholar

28. C. Klabunde and M. Wolter, “Mixed integer linear programming time-series based redispatch optimization”, in: IEEE PES ISGT-Europe, 26–28 Oct. 2020.10.1109/ISGT-Europe47291.2020.9248954Search in Google Scholar

29. V. H. Hinojosa and F.Gonzalez-Longatt, “Preventive security-constrained DCOPF formulation using power transmission distribution factors and line outage distribution factors”, Energies, MDPI AG, vol. 11(6), 2018.10.3390/en11061497Search in Google Scholar

Erhalten: 2021-01-20
Angenommen: 2021-02-19
Online erschienen: 2021-03-20
Erschienen im Druck: 2021-05-26

© 2021 Walter de Gruyter GmbH, Berlin/Boston

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