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Iranian Journal of Electrical and Electronic Engineering، جلد ۱۶، شماره ۳، صفحات ۳۷۱-۳۹۲
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چکیده فارسی مقاله
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عنوان انگلیسی Stochastic Joint Optimal Distributed Generation Scheduling and Distribution Feeder Reconfiguration of Microgrids Considering Uncertainties Modeled by Copula-Based Method
چکیده انگلیسی مقاله Using distributed generations (DGs) with optimal scheduling and optimal distribution feeder reconfiguration (DFR) are two aspects that can improve efficiency as well as technical and economic features of microgrids (MGs). This work presents a stochastic copula scenario-based framework to jointly carry out optimal scheduling of DGs and DFR. This framework takes into account non-dispatchable and dispatchable DGs. In this paper, the dispatchable DG is a fuel cell unit and the non-dispatchable DGs with stochastic generation are wind turbines and photovoltaic cells. The uncertainties of wind turbine and photovoltaic generations, as well as electrical demand, are formulated by a copula-based method. The generation of scenarios is carried out by the scenario tree method and representative scenarios are nominated with scenario reduction techniques. To obtain a weighted solution among the various solutions made by several scenarios, the average stochastic output (ASO) index is used.  The objective functions are minimization of the operational cost of the MG, minimization of active power loss, maximization of voltage stability index, and minimization of emissions. The best-compromised solution is then chosen by using the fuzzy technique. The capability of the proposed model is investigated on a 33-bus MG. The simulation results show the efficiency of the proposed model to optimize objective functions, while the constraints are satisfied.
کلیدواژه‌های انگلیسی مقاله
نویسندگان مقاله | M. Khajevand
Department of Electrical, Biomedical, and Mechatronics Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran.


| A. Fakharian
Department of Electrical, Biomedical, and Mechatronics Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran.


| M. Sedighizadeh
Faculty of Electrical Engineering, Shahid Beheshti University, Evin, Tehran, Iran
نشانی اینترنتی http://ijeee.iust.ac.ir/browse.php?a_code=A-10-1742-4&slc_lang=en&sid=1
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کد مقاله (doi)
زبان مقاله منتشر شده en
موضوعات مقاله منتشر شده Artificial Intelligence Techniques
نوع مقاله منتشر شده Research Paper
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