توزیع بار اقتصادی با هدف کاهش هزینه و بهبود قابلیت اطمینان با در نظر گرفتن عدم قطعیت
محورهای موضوعی : مهندسی الکترونیک
سعید ناصری
1
(
گروه برق دانشگاه آزاد اسلامی واحد بوشهر بوشهر ایران
)
مجتبی نجفی
2
(
گروه برق دانشگاه آزاد اسلامی واحد بوشهر بوشهر ایران
)
مصطفی اسماعیل بیگ
3
(
گروه برق دانشگاه آزاد اسلامی واحد بوشهر بوشهر ایران
)
کلید واژه: قابلیت اطمینان, بهینه سازی, هزینه بهره برداری, توزیع بار اقتصادی,
چکیده مقاله :
هدف از مسئله توزیع بار اقتصادی یافتن یک طرح مطلوب، برای خروجی نیروگاهها به منظور تامین بار مصرفی در یک افق زمانی مشخص می‌باشد، به طوری که تضمین می‌کند طرح پیشنهاد شده تقاضای بار را در سطح قابل قبولی از قابلیت اطمینان برآورده خواهد کرد. با پیدایش روش‌های مدیریت انرژی و مصرف توان الکتریکی، روند رشد بار کاهش یافته اما همچنان نیاز به بهینه سازی مسائل بهره برداری بهینه دربخشهای تولید، شبکه انتقال و توزیع برای تامین و انتقال توان از تولیدکنندگان به مصرف-کنندگان وجود دارد. لذا حل مسئله توزیع بار اقتصادی به صورت بهینه و در سطح قابل قبولی از قابلیت اطمینان ضروری می‌باشد. هدف اصلی این مسئله کاهش هزینه بهره‌برداری می‌باشد. هزینه بهره‌برداری شامل هزینه عملکرد نیروگاهها و نگهداری شبکه می-باشد. علاوه بر هزینه اهداف دیگری نظیر قابلیت اطمینان شبکه نیز وجود دارد که به صورت دقیق و جدی بررسی نشده اند، لذا در این مقاله این اهداف بررسی می شوند. در این مقاله یک چارچوب بهینه سازی چند هدفه ایمن برای دیسپاچینگ نیروگاهها ارائه گردید.کمینه سازی هزینه، انرژی توزیع نشده به عنوان توابع هدف بهینه سازی در نظر گرفته شدند.
The purpose of the economic load distribution problem is to find a suitable design for the output of power plants in order to supply the consumption load in a certain time horizon, so that it ensures that the proposed design will meet the load demand at an acceptable level of reliability. With the advent of energy management and power consumption methods, the load growth trend has decreased, but there is still a need to optimize the issues of optimal operation in the production, transmission and distribution networks to supply and transfer power from producers to consumers. Therefore, solving the problem of economic load distribution in an optimal way and at an acceptable level of reliability is essential. The main purpose of this issue is to reduce operating costs. Operating costs include the cost of operating power plants and maintaining the network. In addition to cost, there are other goals such as network reliability that have not been carefully and seriously examined, so this article examines these goals. In this paper, a safe multi-objective optimization framework for dispatching power plants is presented. Cost minimization, redistributed energy were considered as optimization objective functions.
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[14]M. Ghiassi, H. Saidane, and D. K. Zimbra, “A dynamic artificial neural network model for forecasting time series events,” Int. J. Forecast., vol. 21, no. 2, pp. 341–362, 2005.
[15]L. Wang and C. Singh, “Reserve-constrained multiarea environmental/economic dispatch based on particle swarm optimization with local search,” Eng. Appl. Artif. Intell., vol. 22, no. 2, pp. 298–307, 2009.
[16]R. Roy and S. P. Ghoshal, “A novel crazy swarm optimized economic load dispatch for various types of cost functions,” Int. J. Electr. Power Energy Syst., vol. 30, no. 4, pp. 242–253, 2008.
[17]A. I. S. Kumar, K. Dhanushkodi, J. J. Kumar, and C. K. C. Paul, “Particle swarm optimization solution to emission and economic dispatch problem,” IEEE Reg. 10 Annu. Int. Conf. Proceedings/TENCON, vol. 1, pp. 435–439, 2003.
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[22]Y. P. Zhou, L. J. Tang, J. Jiao, D. D. Song, J. H. Jiang, and R. Q. Yu, “Modified particle swarm optimization algorithm for adaptively configuring globally optimal classification and regression trees,” J. Chem. Inf. Model., vol. 49, no. 5, pp. 1144–1153, 2009.
[23]B. G. Mehr and A. L. A. Mohamare, “Economic Dispatch of Thermal Units considering Valve-point Effect using Learning Backtracking Search Optimization Algorithm” vol. 8, no. 4, pp. 418-426, 2017.
[24]T. L. Laubst, “Reliability evaluation of power systems, Roy Billington and Ronald N. Allan, Plenum Press, New York and London, 1984,” Qual. Reliab. Eng. Int., vol. 1, no. 2, pp. 141–141, 1985.
[25]H. Heitsch and W. Römisch, “Scenario reduction algorithms in stochastic programming,” Comput. Optim. Appl., vol. 24, no. 2–3, pp. 187–206, 2003.
[26]N. A. Belyaev, N. V. Korovkin, O. V. Frolov, and V. S. Chudnyi, “Methods for optimization of power-system operation modes,” Russ. Electr. Eng., vol. 84, no. 2, pp. 74–80, 2013.
[27]Y. Lei et al., “Multi-stage stochastic planning of regional integrated energy system based on scenario tree path optimization under long-term multiple uncertainties,” Appl. Energy, vol. 300, pp. 117-124, Oct. 2021.
[28]K. Vaisakh and L. R. Srinivas, “Genetic evolving ant direction HDE for OPF with non-smooth cost functions and statistical analysis,” Expert Syst. Appl., vol. 38, no. 3, pp. 2046–2062, 2011.
_||_[1]A. J. Mezger and K. C. de Almeida, “Short term hydrothermal scheduling with bilateral transactions via bundle method,” Int. J. Electr. Power Energy Syst., vol. 29, no. 5, pp. 387–396, Jun. 2007.
[2]S. J. P. S. Mariano, J. P. S. Catalão, V. M. F. Mendes, and L. A. F. M. Ferreira, “Profit-based short-term hydro scheduling considering head-dependent power generation,” IEEE Lausanne POWERTECH, Proc.,2007, pp. 1362–1367.
[3]J. L. Martínez Ramos, A. Troncoso Lora, J. Riquelme Santos, and A. Gómez Expósito, “Short-term hydro-thermal coordination based on interior point nonlinear programming and genetic algorithms,” IEEE Porto Power Tech Proc., vol. 3, no. 2, pp. 78–83, 2001.
[4]G. W. Chang et al., “Based Approaches on Short-Term Hydro Scheduling,” IEEE Trans. Power Syst., vol. 16, no. 4, pp. 743–749, 2001.
[5]S. Wi, R. I. An, S. Chang, I. Fong, and B. L. Peter, “Downloaded from https://iranpaper.ir,” vol. 5, no. 3,pp.78-86, 1990.
[6]E. C. Finardi, E. L. Da Silva, and C. Sagastizábal, “Solving the unit commitment problem of hydropower plants via lagrangian relaxation and sequential quadratic programming,” Comput. Appl. Math., vol. 24, no. 3, pp. 317–341, 2005.
[7]C. Samudi, G. P. Das, P. C. Ojha, T. S. Sreeni, and S. Cherian, “Hydro thermal scheduling using particle swarm optimization,” Transm. Distrib. Expo. Conf. IEEE PES Powering Towar. Futur. PIMS, 2008, pp. 3–7.
[8]H. M. Z. Iqbal, A. Ashraf, and A. Ahmad, “Power economic dispatch using particle swarm optimization,” Power Gener. Syst. Renew. Energy Technol. PGSRET ,2015, pp.1-4.
[9]P. Venkatesh, R. Gnanadass, and N. P. Padhy, “Comparison and application of evolutionary programming techniques to combined economic emission dispatch with line flow constraints,” IEEE Trans. Power Syst., vol. 18, no. 2, pp. 688–697, 2003.
[10]D. C. Walters and G. B. Sheble, “Genetic algorithm solution of economic dispatch with valve point loading,” IEEE Trans. Power Syst., vol. 8, no. 3, pp. 1325–1332, 1993.
[11]M. Basu, “A simulated annealing-based goal-attainment method for economic emission load dispatch of fixed head hydrothermal power systems,” Int. J. Electr. Power Energy Syst., vol. 27, no. 2, pp. 147–153, 2005.
[12]M. Dorigo and C. Blum, “Ant colony optimization theory: A survey,” Theor. Comput. Sci., vol. 344, no. 2–3, pp. 243–278, 2005.
[13]R. A. Gallego, R. Romero, and A. J. Monticelli, “Tabu search algorithm for network synthesis,” IEEE Trans. Power Syst., vol. 15, no. 2, pp. 490–495, 2000.
[14]M. Ghiassi, H. Saidane, and D. K. Zimbra, “A dynamic artificial neural network model for forecasting time series events,” Int. J. Forecast., vol. 21, no. 2, pp. 341–362, 2005.
[15]L. Wang and C. Singh, “Reserve-constrained multiarea environmental/economic dispatch based on particle swarm optimization with local search,” Eng. Appl. Artif. Intell., vol. 22, no. 2, pp. 298–307, 2009.
[16]R. Roy and S. P. Ghoshal, “A novel crazy swarm optimized economic load dispatch for various types of cost functions,” Int. J. Electr. Power Energy Syst., vol. 30, no. 4, pp. 242–253, 2008.
[17]A. I. S. Kumar, K. Dhanushkodi, J. J. Kumar, and C. K. C. Paul, “Particle swarm optimization solution to emission and economic dispatch problem,” IEEE Reg. 10 Annu. Int. Conf. Proceedings/TENCON, vol. 1, pp. 435–439, 2003.
[18]B. Zhao and Y. J. Cao, “Multiple objective particle swarm optimization technique for economic load dispatch,” J. Zhejiang Univ. Sci., vol. 6 A, no. 5, pp. 420–427, 2005.
[19]T. A. A. Victoire and A. E. Jeyakumar, “Reserve constrained dynamic dispatch of units with valve-point effects,” IEEE Trans. Power Syst., vol. 20, no. 3, pp. 1273–1282, 2005.
[20]M. Neyestani, M. M. Farsangi, H. Nezamabadipour, and K. Y. Lee, “A modified particle swarm optimization for economic dispatch with nonsmooth cost functions,” IFAC Proc. Vol., vol. 42, no. 9, pp. 267–272, 2009.
[21]K. Y. Lee and M. A. El-Sharkawi, “Modern Heuristic Optimization Techniques: Theory and Applications to Power Systems,” Mod. Heuristic Optim. Tech. Theory Appl. to Power Syst., pp. 1–586, 2007.
[22]Y. P. Zhou, L. J. Tang, J. Jiao, D. D. Song, J. H. Jiang, and R. Q. Yu, “Modified particle swarm optimization algorithm for adaptively configuring globally optimal classification and regression trees,” J. Chem. Inf. Model., vol. 49, no. 5, pp. 1144–1153, 2009.
[23]B. G. Mehr and A. L. A. Mohamare, “Economic Dispatch of Thermal Units considering Valve-point Effect using Learning Backtracking Search Optimization Algorithm” vol. 8, no. 4, pp. 418-426, 2017.
[24]T. L. Laubst, “Reliability evaluation of power systems, Roy Billington and Ronald N. Allan, Plenum Press, New York and London, 1984,” Qual. Reliab. Eng. Int., vol. 1, no. 2, pp. 141–141, 1985.
[25]H. Heitsch and W. Römisch, “Scenario reduction algorithms in stochastic programming,” Comput. Optim. Appl., vol. 24, no. 2–3, pp. 187–206, 2003.
[26]N. A. Belyaev, N. V. Korovkin, O. V. Frolov, and V. S. Chudnyi, “Methods for optimization of power-system operation modes,” Russ. Electr. Eng., vol. 84, no. 2, pp. 74–80, 2013.
[27]Y. Lei et al., “Multi-stage stochastic planning of regional integrated energy system based on scenario tree path optimization under long-term multiple uncertainties,” Appl. Energy, vol. 300, pp. 117-124, Oct. 2021.
[28]K. Vaisakh and L. R. Srinivas, “Genetic evolving ant direction HDE for OPF with non-smooth cost functions and statistical analysis,” Expert Syst. Appl., vol. 38, no. 3, pp. 2046–2062, 2011.
