| 摘要: |
| 风电场分布的地域多元化能够平滑风电波动。提出基于copula函数和均值－方差模型研究分布在不同位置风电场风速空间相关性和最优风电分配。利用极大似然法选取合适的copula函数描述风速间的相关关系，计算出风速间基于copula的秩相关系数，并借助最小二乘法拟合秩相关系数和风电场距离的关系。构造适合风电场的均值－方差模型优化风电的分配，其中风场的容量因子表示收益，风电前后时刻出力变化的标准差表示风险，线性相关系数用秩相关系数代替。以荷兰40个风场为例，结果表明，Gumbel copula 和 t copula函数较好地拟合了风场间风速的相关关系，并且，随着距离每增加100 km，秩相关系数下降0.1；通过求解均值－方差模型，得到各风场风电最优组合，相对于单个风场，风电波动下降程度最大达到70%，海上风场在降低风电波动中作用较大，在此模型指导下，可以选择最优风电分配策略，降低风电波动给系统带来的风险和成本。 |
| 关键词: 风电场 风电波动 风速相关性 copula 风电分配 均值－方差模型 |
| DOI：10.7667/j.issn.1674-3415.2013.19.017 |
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| 基金项目:国家自然科学基金资助项目(71061002)；广西自然科学基金资助项目(2011GXNSFD018022)；广西高校人才小高地建设创新团队资助计划 |
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| Wind speed spatial correlation and optimal wind power allocation |
| JIAN Jin-bao1,2,LIU Si-dong1,3 |
| () |
| Abstract: |
| Geographic diversification of wind farms can smooth out the variability of wind power. The paper applies copula function and mean-variance model to study the wind speed spatial correlation and optimal wind power allocation. The maximum likelihood method is utilized to choose appropriate copula function to describe the correlation of wind speeds, and the pairwise rank correlation coefficients of wind speeds are calculated by copula, while the relationship between rank correlation and wind farms distance is fitted by least squares method. A new mean-variance model is constructed to optimize wind power allocation, where return is defined as the capacity factor, and risk is defined as the standard deviation of hourly wind power variation, and linear correlation is replaced by rank correlation. Taking Holland 40 wind farms as an example, the results show that Gumbel copula and t copula present a better fit for wind speeds correlation, and the rank correlation tends to decrease by 0.1 with a increasing distance of 100 km. By solving the mean-variance model, the optimal combination of wind power is obtained, and wind power variability drops by a maximum of 70% comparing with the single wind farm, where off-shore wind farms play a more important role. Under the guidance of this model, an optimal wind power allocation strategy can be used to reduce the system risk and cost due to wind power variability. |
| Key words: wind farms wind power variability correlation of wind speeds copula wind power portfolios mean-variance model |
