电力系统保护与控制
電力繫統保護與控製
전력계통보호여공제
POWER SYSTM PROTECTION AND CONTROL
2013年
19期
110-117
,共8页
风电场%风电波动%风速相关性%copula%风电分配%均值-方差模型
風電場%風電波動%風速相關性%copula%風電分配%均值-方差模型
풍전장%풍전파동%풍속상관성%copula%풍전분배%균치-방차모형
wind farms%wind power variability%correlation of wind speeds%copula%wind power portfolios%mean-variance model
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。
風電場分佈的地域多元化能夠平滑風電波動。提齣基于copula函數和均值-方差模型研究分佈在不同位置風電場風速空間相關性和最優風電分配。利用極大似然法選取閤適的copula函數描述風速間的相關關繫，計算齣風速間基于copula的秩相關繫數，併藉助最小二乘法擬閤秩相關繫數和風電場距離的關繫。構造適閤風電場的均值-方差模型優化風電的分配，其中風場的容量因子錶示收益，風電前後時刻齣力變化的標準差錶示風險，線性相關繫數用秩相關繫數代替。以荷蘭40箇風場為例，結果錶明，Gumbel copula 和 t copula函數較好地擬閤瞭風場間風速的相關關繫，併且，隨著距離每增加100 km，秩相關繫數下降0.1；通過求解均值-方差模型，得到各風場風電最優組閤，相對于單箇風場，風電波動下降程度最大達到70%，海上風場在降低風電波動中作用較大，在此模型指導下，可以選擇最優風電分配策略，降低風電波動給繫統帶來的風險和成本。
풍전장분포적지역다원화능구평활풍전파동。제출기우copula함수화균치-방차모형연구분포재불동위치풍전장풍속공간상관성화최우풍전분배。이용겁대사연법선취합괄적copula함수묘술풍속간적상관관계，계산출풍속간기우copula적질상관계수，병차조최소이승법의합질상관계수화풍전장거리적관계。구조괄합풍전장적균치-방차모형우화풍전적분배，기중풍장적용량인자표시수익，풍전전후시각출력변화적표준차표시풍험，선성상관계수용질상관계수대체。이하란40개풍장위례，결과표명，Gumbel copula 화 t copula함수교호지의합료풍장간풍속적상관관계，병차，수착거리매증가100 km，질상관계수하강0.1；통과구해균치-방차모형，득도각풍장풍전최우조합，상대우단개풍장，풍전파동하강정도최대체도70%，해상풍장재강저풍전파동중작용교대，재차모형지도하，가이선택최우풍전분배책략，강저풍전파동급계통대래적풍험화성본。
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.
