Structure Optimization of Stand-Alone Renewable Power Systems Based on Multi Object Function

Jae-Hoon Cho, Myung-Geun Chun and Won-Pyo Hong
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Jae-Hoon Cho: Smart Logistics Technology Institute, Hankyong National University, 327 Chungang-ro Anseong-si, Kyonggi-do 17579, Korea
Myung-Geun Chun: Department of Electronics Engineering, Chungbuk National University, Chungdae-ro, Seowon-Gu, Cheongju, Chungbuk 28644, Korea
Won-Pyo Hong: Department of Building Services Engineering, Hanbat National University, 125 Dongseodae-ro, Yuseong-Gu, Daejeon 34158, Korea

Energies, 2016, vol. 9, issue 8, 1-19

Abstract: This paper presents a methodology for the size optimization of a stand-alone hybrid PV/wind/diesel/battery system while considering the following factors: total annual cost (TAC), loss of power supply probability (LPSP), and the fuel cost of the diesel generator required by the user. A new optimization algorithm and an object function (including a penalty method) are also proposed; these assist with designing the best structure for a hybrid system satisfying the constraints. In hybrid energy system sources such as photovoltaic (PV), wind, diesel, and energy storage devices are connected as an electrical load supply. Because the power produced by PV and wind turbine sources is dependent on the variation of the resources (sun and wind) and the load demand fluctuates, such a hybrid system must be able to satisfy the load requirements at any time and store the excess energy for use in deficit conditions. Therefore, reliability and cost are the two main criteria when designing a stand-alone hybrid system. Moreover, the operation of a diesel generator is important to achieve greater reliability. In this paper, TAC, LPSP, and the fuel cost of the diesel generator are considered as the objective variables and a hybrid teaching–learning-based optimization algorithm is proposed and used to choose the best structure of a stand-alone hybrid PV/wind/diesel/battery system. Simulation results from MATLAB support the effectiveness of the proposed method and confirm that it is more efficient than conventional methods.

Keywords: stand-alone hybrid PV/wind/diesel/battery system; teaching-learning-based optimization algorithm; total annual cost; loss of power supply probability (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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