Multi-objective Dynamic Planning of Substations and Primary Feeders Considering Uncertainties and Reliability

International Journal of Smart Electrical Engineering

مقاله 1، دوره 05، شماره 02، شهریور 2016، صفحه 59-73   

نوع مقاله: Research Paper

نویسندگان

Masoumeh Karimi¹؛ Mahmoud Reza Haghifam^{* 2}

¹Department of Electrical Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran

²Faculty of Electrical and Computer Engineering, Tarbiat Modares University, P.O. Box 144115-199, Tehran, Iran

چکیده

This research uses a comprehensive method to solve a combinatorial problem of distribution network expansion planning (DNEP) problem. The proposed multi-objective scheme aims to improve power system's accountability and system performance parameters, simultaneously, in the lowest possible costs. The dynamic programming approach is implemented in order to find the optimal sizing, siting and timing of HV/MV substations, feeders and distributed generations. Based on the input data, the results should be closer to the reality. So, the relevant uncertainties must well incorporate in DNEP modeling to achieve the best possible strategy. The most important uncertainties are the load forecasting, market price errors as well as the uncertainties related to the intermittent nature of the output power of renewable energy resources. Given that DNEP is a multi-objective optimization problem including several objective functions such as: cost based function, voltage deviation, voltage stability factor and measuring the amount of produced emission. NSGA-II as an appropriate alternative results several non-dominated solutions where finally fuzzy set theory is used to select the best compromise solution among them. The proposed scheme is applied to 54-bus system distribution network. The comparison study validates the efficiency of suggested method in the presence of distributed generations.

کلیدواژه‌ها

Dynamic Expansion Planning؛ Feeder Routing؛ DG allocation؛ uncertainty؛ Reliability؛ Multi-objective optimization