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Probabilistic Optimal PV Capacity Planning for Wind Farm Expansion Based on NASA Data

机译:基于NASA数据的风电场扩容的概率最优光伏容量规划

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Considering the complementary characteristics of wind and solar energy, expanding an existing wind farm with photovoltaic panels can significantly smooth fluctuation of output power and improve operation economy. This paper proposes a two-stage approach to optimize the wind farm expansion. Based on the National Aeronautics and Space Administration data, modified meteorological models are developed considering the correlation between wind speed and solar irradiation. Taking into account fluctuation of output power, utilization of electrical equipment, and losses of renewable energy, a multi-objective optimization model is established. Two scenarios with different transformer ratings are analyzed to determine whether to expand electrical equipment. The Monte Carlo simulation is utilized to generate meteorological data in the first stage. The Pareto optimal solution set is searched by the multi-objective particle swarm optimization algorithm to determine the final solution in the second stage. A case study was conducted to validate the proposed approach.
机译:考虑到风能和太阳能的互补特性,利用光伏面板扩展现有的风电场可以显着缓解输出功率的波动并提高运营经济性。本文提出了一种分两个阶段的方法来优化风电场的扩展。根据美国国家航空航天局的数据,考虑到风速和太阳辐射之间的相关性,开发了改进的气象模型。考虑到输出功率的波动,电气设备的利用率以及可再生能源的损失,建立了一个多目标优化模型。分析了具有不同变压器额定值的两种情况,以确定是否扩展电气设备。在第一阶段,利用蒙特卡洛模拟生成气象数据。通过多目标粒子群优化算法搜索Pareto最优解集,以确定第二阶段的最终解。进行了案例研究以验证所提出的方法。

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