利用发电充分性研究微电网中最优可再生能源发电容量

S. Kahrobaee, S. Asgarpoor, Milad Kahrobaee
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引用次数: 8

摘要

微电网作为小型电力系统,可以由不同类型的负荷和分布式发电组成。随着可再生能源发电一体化程度的提高,由于设备故障的影响和这些资源的间歇性,系统的总可用发电能力将会进一步降低。因此,确定最佳的可再生能源发电容量并提供足够的备用余量以满足微电网的目标可靠性是至关重要的。在本文中,我们首先建立了一个微电网模型,包括常规和可再生分布式发电和负荷。其次,我们通过发电充足性研究确定了在一定电网可靠性水平下满足需求增长所需的可再生能源发电容量。利用负荷损失概率(LOLP)和预期未服务能量(EENS)等参数来评估微电网的充分性。第三,通过敏感性分析,研究风速多样性(通过关联风电输出获取)、风能和太阳能的组合以及负荷多样性等不同条件对发电充分性的影响。最后,确定最优的可再生能源发电容量,使发电和未服务电力的总成本最小化。优化过程基于粒子群优化(PSO)方法,在每次迭代中使用蒙特卡罗(MC)模拟进行代充分性研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimum renewable generation capacities in a microgrid using generation adequacy study
Microgrids, as small power systems, may be comprised of different types of loads and distributed generation. As the integration of renewable power generation increases, the total available generation capacity of the system will be more derated due to the effect of equipment failures and the intermittent nature of these resources. Therefore, it is critical to determine optimum renewable generation capacities and provide enough reserve margin to meet the target reliability of the microgrid. In this paper, we first model a microgrid, including conventional and renewable distributed generation and the loads. Second, we determine the renewable generation capacity required to meet growth in demand at a certain level of grid reliability through a generation adequacy study. Adequacy of the microgrid is evaluated using parameters such as loss of load probability (LOLP) and expected energy not served (EENS). Third, the impact of different conditions, such as wind speed diversity (captured by correlating the wind power output), a combination of wind and solar power, and load diversity, on generation adequacy is studied through sensitivity analyses. Finally, the optimum renewable generation capacities are determined such that the total cost of generation and unserved power is minimized. The optimization process is based on the particle swarm optimization (PSO) method which uses Monte Carlo (MC) simulation for generation adequacy studies in each iteration.
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