A novel framework for optimization of a grid independent hybrid renewable energy system: A case study of Iran
A novel framework for optimization of a grid independent hybrid renewable energy system: A case study of Iran
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DOI：
10.1016/j.solener.2014.12.013
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发表时间：
2015-02
期刊：
影响因子：
6.7
通讯作者：
A. Askarzadeh;L. Coelho
中科院分区：
文献类型：
--
作者：
A. Askarzadeh;L. Coelho
本文建立了一个优化模型，以确定独立的混合可再生能源系统(HRES)对伊朗科尔曼偏远地区的电气化的最佳规模。该模型基于与系统部件相关的三个决策变量，即光伏电池板的总面积(连续变量)、旋转涡轮叶片的总扫掠面积(连续变量)和蓄电池组的数量(整数变量)。为了寻找变量的最优值，提出了粒子群优化算法(PSO)及其变种。由于规划问题的非线性和非凸性，粒子群算法作为一种有效的基于种群的启发式算法是一个很好的候选者。粒子群算法对搜索空间进行搜索，使系统的寿命周期费用最小，同时保证一定程度的系统可靠性。仿真结果表明，基于惯性权重的自适应PSO算法比其他PSO算法具有更高的性价比。
In this paper, an optimization model is developed to determine the best size of a stand-alone hybrid renewable energy system (HRES) for electrification to a remote area located in Kerman, Iran. The model is defined based on three decision variables related to the system components, namely, total area occupied by the set of PV panels (a continuous variable), total swept area by the rotating turbines’ blades (a continuous variable) and the number of batteries (an integer variable). In order to find the optimal values of the variables, particle swarm optimization (PSO) and some of its variants are proposed. Due to the non-linearity and non-convexity of the sizing problem, PSO which is an efficient population-based heuristic technique can be a good candidate. Particles of PSO probe the search space to minimize the life cycle cost (LCC), ensuring at the same time certain level of system reliability. Simulation results reveal that the PV/WT/battery system is the most cost-effective one and adaptive inertia weight-based PSO algorithm yields more promising results than the other PSO variants.