考虑风电不确定性的含海上风电场电力系统优化调度策略研究

张晋华; 朱悦榕; 李旭强; 刘良雨

doi:10.16513/j.2096-2185.DE.2106547

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分布式能源 ›› 2021, Vol. 6 ›› Issue (5) : 33-43. DOI: 10.16513/j.2096-2185.DE.2106547

海上风电专题

考虑风电不确定性的含海上风电场电力系统优化调度策略研究

张晋华 ¹^,² ,
朱悦榕 ¹ ,
李旭强 ¹ ,
刘良雨 ¹

作者信息 +

Optimization Scheduling Strategy of Offshore Wind Farm Power System Considering Wind Power Uncertainty

Author information +

文章历史 +

摘要

由于风电具有波动性和间歇性，当海上风电机组并网时，电力系统将会受到冲击，导致电网频率波动和运行控制的失调，因此需要安排其他发电机组来进行调节。在考虑海上风电机组不确定性的情况下，建立风火联合优化调度模型，以风电为主体，对电力系统优化调度策略进行研究。首先，用不同Copula函数对海上风电场功率进行拟合分析，选定t-Copula函数进行风电功率预测。在此基础上，建立了多目标的风火联合优化调度模型，将乌鸦搜索优化算法与粒子群优化算法进行收敛速度对比，选取乌鸦搜索优化算法对分析数据进行优化处理，制定全局优化调度方案。最后，算例验证了所提模型的有效性。

Abstract

Since wind power is volatile and intermittent, when offshore wind turbines are connected to the grid, the power system will be shocked, resulting in frequency fluctuations and dysregulation of the grid operation control, so other generating units need to be arranged for regulation. In this paper, considering the uncertainty of offshore wind turbines, a wind-fire joint optimal scheduling model is established to study the optimal scheduling strategy of the power system with wind power as the main body. First, different Copula functions are used to fit and analyze the offshore wind farm power, and the t-Copula function is selected for wind power prediction. On this basis, a multi-objective wind-fire joint optimal scheduling model is established, the convergence speed of the crow search optimization algorithm is compared with the particle swarm optimization algorithm, and the crow search optimization algorithm is selected to optimize the analyzed data and develop a global optimal scheduling scheme. Finally, the arithmetic example verifies the effectiveness of the proposed model.

导出引用

张晋华, 朱悦榕, 李旭强, 等. 考虑风电不确定性的含海上风电场电力系统优化调度策略研究[J]. 分布式能源. 2021, 6(5): 33-43 https://doi.org/10.16513/j.2096-2185.DE.2106547

Jinhua ZHANG, Yuerong ZHU, Xuqiang LI, et al. Optimization Scheduling Strategy of Offshore Wind Farm Power System Considering Wind Power Uncertainty[J]. Distributed Energy Resources. 2021, 6(5): 33-43 https://doi.org/10.16513/j.2096-2185.DE.2106547

中图分类号： TK81

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