A Genetic Optimization Algorithm as Applied to the Power Plant Load Distribution Problem
Abstract
The article discusses a genetic optimization algorithm and its application for the problem of distributing the total electric and thermal loads of a power plant among the operating units of a combined heat and power plant (CHP) and the total electric load among the operating units of a condensing thermal power plant (CTPP). The need to solve this problem is stemming from the fact that, on the one hand, the power plants operate presently for a significant period of time with a load below its nominal level and, on the other hand, the saving of fuel and energy has become a real challenge under the present economic and environmental conditions. The mathematical models of the fuel consumption characteristics of power units (obtained from the results of thermal tests and having the form of second-order polynomials) are used as initial data for the analysis, and constraints are imposed on the total electric and thermal loads and on the loads of individual power units which are determined by the equipment operation process schedule. A case is presented in which some of the leading coefficients in the model polynomials are negative, i.e., the Lagrange multiplier method is not applicable. The overall consumption of fuel for the operating units is used as the objective function. For a specified composition of power plant equipment, the optimum loads of the power units are determined using a genetic algorithm, and the advantages of the genetic algorithm method in comparison with other methods used for solving this problem (e.g., the dynamic programming method and the Lagrange multiplier method) are shown. The genetic algorithm embodiment as applied to the considered case is presented (the problem for the CTPP was solved for a five-unit plant, and that for the CHP was solved for a three-unit plant). The article describes how the initial population is generated, how the crossing operation is carried out, how the least suited population members are rejected, and how new members are included in the population. Calculations for a few CTPP and CHP total loading cases are carried out. It is shown that application of the genetic algorithm for solving the formulated problem makes it possible to obtain economic gains due to determining the optimum power unit loadings. The economic gain for the CTPP for the total fuel consumption per hour in comparison with the uniformly distributed load is on average 2.3 %, and the gain for the CHP is up to 11 % for the same conditions. Recommendations on using the genetic algorithm in searching for the objective function minimum are given.
References
2. Kilic A.N. Safety Assurance of NPP with VVER // Proc 9th Intern. Sci. and Tech. Conf. Considerations on Design and Operation of NPP in Flexible (Non-baseload) Mode (Overview of the IAEA Guideline). Podolsk, 2015. P. 28.
3. Аракелян Э.К., Пикина Г.А. Оптимизация и оптимальное управление. М.: Изд-во МЭИ, 2003.
4. Мерзликина Е.И. Оптимизация распределения тепловых и электрических нагрузок между энергоблоками ТЭС с учетом неопределенности исходной информации: дисс....канд. тех. наук. М.: МЭИ, 2004.
5. Щедеркина Т.Е., Мерзликина Е.И. Учет неопределенности исходных данных при решении задачи статической оптимизации // Вестник МЭИ. 2006. № 3. С. 22—28.
6. Болонов В.О., Аракелян Э.К. Оптимальное управление режимами работы оборудования ТЭЦ с ПГУ // Теплоэнергетика. 2007. № 11. С. 69—77
7. Рутковская Д., Пилиньский М., Рутковский Л. Нейронные сети, генетические алгоритмы и нечеткие системы. М.: Горячая линия — Телеком, 2006.
8. Aruna Y., Sanjeev K. Genetic Algorithm for Optimizing Load Distribution on Video on Demand Servers // Intern. J. Sci. Eng. Research. 2012. V. 3. Iss. 6.
9. Sedighizadeh M., Rezazadeh A. Using Genetic Algorithm for Distributed Generation Allocated to Reduce Losses and Improve Voltage Profile // World Academy of Science, Engineering and Technology. 2008. V. 37. Pp. 251—256.
10. Tan K.C., Li Y. Performance-based Control System Design Automation via Evolutionary Computing// Engineering Appl. of Artificial Intelligence. 2001. V. 14. Pp. 473—486.
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Для цитирования: Мерзликина Е.И., Щедеркина Т.Е. Генетический алгоритм оптимизации в задаче распределения нагрузки тепловойэлектростанции // Вестник МЭИ. 2018. № 1. С. 22—28. DOI: 10.24160/1993-6982-2018-1-22-28.
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1. Arakelyan E.K. i dr. Metodicheskie Podkhody k Optimal'nomu Upravleniyu Rezhimami Raboty TETS so Slozhnym Sostavom Oborudovaniya. Teploenergetika. 2012;10:12—18. (in Russian).
2. Kilic A.N. Safety Assurance of NPP with VVER. Proc 9th Intern. Sci. and Tech. Conf. Considerations on Design and Operation of NPP in Flexible (Non-baseload) Mode (Overview of the IAEA Guideline). Podolsk, 2015:28.
3. Arakelyan E.K., Pikina G.A. Optimizatsiya i Optimal'noe Upravlenie. M.: Izd-vo MPEI, 2003. (in Russian).
4. Merzlikina E.I. Optimizatsiya Raspredeleniya Teplovykh i Elektricheskikh Nagruzok Mezhdu Energo- blokami TES s Uchetom Neopredelennosti Iskhodnoy In- formatsii: Diss....Kand. Tekh. Nauk. M.: MPEI, 2004. (in Russian).
5. Shchederkina T.E., Merzlikina E.I. Uchet Neopredelennosti Iskhodnykh Dannykh pri Reshenii Zadachi Staticheskoy Optimizatsii. Vestnik MPEI. 2006;3:22—28. (in Russian).
6. Bolonov V.O., Arakelyan E.K. Optimal'noe Upravlenie Rezhimami Raboty Oborudovaniya TETS s PGU. Teploenergetika.2007;11:69—77. (in Russian).
7. Rutkovskaya D., Pilin'skiy M., Rutkovskiy L. Neyronnye Seti, Geneticheskie Algoritmy i Nechetkie Sistemy. M.: Goryachaya Liniya — Telekom, 2006. (in Russian).
8. Aruna Y., Sanjeev K. Genetic Algorithm for Optimizing Load Distribution on Video on Demand Servers. Intern. J. Sci. Eng. Research. 2012;3;6.
9. Sedighizadeh M., Rezazadeh A. Using Genetic Algorithm for Distributed Generation Allocated to Reduce Losses and Improve Voltage Profile. World Academy of Science, Engineering and Technology. 2008;37:251—256.
10. Tan K.C., Li Y. Performance-based Control System Design Automation via Evolutionary Computing. Engineering Appl. of Artificial Intelligence. 2001;14:473—486.
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For citation: Merzlikina E.I., Shchederkina T.E. A Genetic Optimization Algorithm as Applied to the Power Plant Load Distribution Problem. MPEI Vestnik. 2018;1:22—28. (in Russian). DOI: 10.24160/1993-6982-2018-1-22-28.
