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Rahimi Moqaddam, Milad, Asle Haddad, Ahmad, Ramezanian, Reza. (1397). A Model For Electricity Generation Expansion Planning Problems. , 52(3), 389-403. doi: 10.22059/jieng.2019.226224.1310
Milad Rahimi Moqaddam; Ahmad Asle Haddad; Reza Ramezanian. "A Model For Electricity Generation Expansion Planning Problems". , 52, 3, 1397, 389-403. doi: 10.22059/jieng.2019.226224.1310
Rahimi Moqaddam, Milad, Asle Haddad, Ahmad, Ramezanian, Reza. (1397). 'A Model For Electricity Generation Expansion Planning Problems', , 52(3), pp. 389-403. doi: 10.22059/jieng.2019.226224.1310
Rahimi Moqaddam, Milad, Asle Haddad, Ahmad, Ramezanian, Reza. A Model For Electricity Generation Expansion Planning Problems. , 1397; 52(3): 389-403. doi: 10.22059/jieng.2019.226224.1310

A Model For Electricity Generation Expansion Planning Problems

Advances in Industrial Engineering
مقاله 7، دوره 52، شماره 3، دی 2018، صفحه 389-403    اصل مقاله (921.47 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/jieng.2019.226224.1310
نویسندگان
Milad Rahimi Moqaddam1؛ Ahmad Asle Haddad؛ Reza Ramezanian* 2
1Depertment of Industrial Engineering, K. N. Toosi University of Technology
2Assistant Professor in Department of Industrial Engineering, K.N. Toosi University of Technology
چکیده
In this paper , a new mathematical model for the electricity generation expansion planning problem are presented. The model, in terms of planning, is among the long-term planning in their literature. However, the steps of model are on a monthly basis. Considering the soft and hard constraints together and fits with other features is the model concept in the real world. Classifying plants according to construction areas should be added to other model features. Then the exact solution to small - scale of the model is presented by the software (GAMS). Then, due to the complexity of the problem, two meta-heuristic algorithms, genetic and traininig – learninig based optimization, was presented. Genetic algorithm parameters are set using RSM. The 22 series of numerical examples by each of these two algorithms are solved and then the results were compared with each other. The results show that the genetic algorithm has the advantage.
کلیدواژه‌ها
Electric Energy؛ Generation Expansion Planning؛ Genetic Algorithm؛ Teaching –Learning Based Optimization Algorithm
عنوان مقاله [English]
ارائۀ مدل برنامه‌ریزی توسعۀ پایدار نیروگاه‌های تولید انرژی الکتریسیته
نویسندگان [English]
میلاد رحیمی مقدم1؛ احمد اصل حداد؛ رضا رمضانیان2
1کارشناسی ارشد مهندسی صنایع، دانشگاه صنعتی خواجه ‌نصیرالدین طوسی، تهران
2دانشیار مهندسی صنایع، دانشگاه صنعتی خواجه ‌نصیرالدین طوسی، تهران
چکیده [English]
در این مقاله مدل ریاضی جدید برای توسعة نیروگاه‌های انرژی الکتریسیته ارائه شده است که از نظر برنامه‌ریزی در گروه برنامه‌ریزی‌های بلندمدت در ادبیات خود محسوب می‌شود، اما گام‌های مسئله به‌صورت ماهانه است. درنظرگرفتن محدودیت‌های نرم و سخت در کنار یکدیگر و متناسب با مفهوم مدل در دنیای واقعی، از دیگر ویژگی‌های مدل مذکور است. همچنین باید تقسیم‌بندی نیروگاه‌ها براساس مناطق احداث آن‌ها را نیز به دیگر ویژگی‌های مدل افزود. در ادامه حل دقیق مدل در ابعاد کوچک به‌کمک نرم‌افزار گمز صورت گرفته و به‌دلیل پیچیدگی مسئله، الگوریتم‌های فرابتکاری ژنتیک و بهینه‌سازی مبتنی بر آموزش و یادگیری مطرح شده است. تنظیم پارامتر الگوریتم ژنتیک نیز با استفاده از روش RSM انجام شده و سپس 22 سری مثال عددی با هریک از این دو الگوریتم حل شده است. نتایج نیز با یکدیگر مقایسه شده‌اند که نشان‌دهندة برتری الگوریتم ژنتیک هستند.
کلیدواژه‌ها [English]
الگوریتم بهینه‌سازی مبتنی بر آموزش و یادگیری, الگوریتم ژنتیک, انرژی الکتریسیته, توسعة نیروگاه‌های انرژی الکتریسیته
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