Rational distribution network planning optimizes power flow distribution,reduces grid stress,enhances voltage quality,promotes renewable energy utilization,and reduces costs.This study establishes a distribution netwo...Rational distribution network planning optimizes power flow distribution,reduces grid stress,enhances voltage quality,promotes renewable energy utilization,and reduces costs.This study establishes a distribution network planning model incorporating distributed wind turbines(DWT),distributed photovoltaics(DPV),and energy storage systems(ESS).K-means++is employed to partition the distribution network based on electrical distance.Considering the spatiotemporal correlation of distributed generation(DG)outputs in the same region,a joint output model of DWT and DPV is developed using the Frank-Copula.Due to the model’s high dimensionality,multiple constraints,and mixed-integer characteristics,bilevel programming theory is utilized to structure the model.The model is solved using a mixed-integer particle swarmoptimization algorithm(MIPSO)to determine the optimal location and capacity of DG and ESS integrated into the distribution network to achieve the best economic benefits and operation quality.The proposed bilevel planning method for distribution networks is validated through simulations on the modified IEEE 33-bus system.The results demonstrate significant improvements,with the proposedmethod reducing the annual comprehensive cost by 41.65%and 13.98%,respectively,compared to scenarios without DG and ESS or with only DG integration.Furthermore,it reduces the daily average voltage deviation by 24.35%and 10.24%and daily network losses by 55.72%and 35.71%.展开更多
As an effective carrier of integrated clean energy,the microgrid has attracted wide attention.The randomness of renewable energies such as wind and solar power output brings a significant cost and impact on the econom...As an effective carrier of integrated clean energy,the microgrid has attracted wide attention.The randomness of renewable energies such as wind and solar power output brings a significant cost and impact on the economics and reliability of microgrids.This paper proposes an optimization scheme based on the distributionally robust optimization(DRO)model for a microgrid considering solar-wind correlation.Firstly,scenarios of wind and solar power output scenarios are generated based on non-parametric kernel density estimation and the Frank-Copula function;then the generated scenario results are reduced by K-means clustering;finally,the probability confidence interval of scenario distribution is constrained by 1-norm and∞-norm.The model is solved by a column-and-constraint generation algorithm.Experimental studies are conducted on a microgrid system in Jiangsu,China and the obtained scheduling solution turned out to be superior under wind and solar power uncertainties,which verifies the effectiveness of the proposed DRO model.展开更多
在风光等清洁能源渗透率及能源低碳化需求不断提高的背景下,如何精确模拟新能源出力不确定性及引导负荷侧柔性资源参与需求响应显得尤为重要。针对上述问题,本文提出一种计及源荷不确定性及阶梯型碳交易的虚拟电厂优化调度模型。首先,...在风光等清洁能源渗透率及能源低碳化需求不断提高的背景下,如何精确模拟新能源出力不确定性及引导负荷侧柔性资源参与需求响应显得尤为重要。针对上述问题,本文提出一种计及源荷不确定性及阶梯型碳交易的虚拟电厂优化调度模型。首先,源侧基于Frank-Copula函数建立风光出力联合概率分布模型,采样约简得风光出力典型场景。其次,在多能耦合虚拟电厂中引入碳捕集与封存(carbon capture and storage,CCS)设备,降低碳排放,荷侧建立考虑柔性用户用能满意度的需求响应模型,以提升风光消纳。同时,引入阶梯型碳交易机制,建立源荷协同优化及低碳性改造的VPP日前优化调度模型。然后,采用梯形隶属度函数将多目标优化问题模糊化为单目标优化问题,调用CPLEX求解器求解。最后,通过算例分析验证本文方法的有效性。展开更多
基金This research was funded by“Chunhui Program”Collaborative Scientific Research Project of the Ministry of Education of the People’s Republic of China(Project No.HZKY20220242)the S&T Program of Hebei(Project No.225676163GH).
文摘Rational distribution network planning optimizes power flow distribution,reduces grid stress,enhances voltage quality,promotes renewable energy utilization,and reduces costs.This study establishes a distribution network planning model incorporating distributed wind turbines(DWT),distributed photovoltaics(DPV),and energy storage systems(ESS).K-means++is employed to partition the distribution network based on electrical distance.Considering the spatiotemporal correlation of distributed generation(DG)outputs in the same region,a joint output model of DWT and DPV is developed using the Frank-Copula.Due to the model’s high dimensionality,multiple constraints,and mixed-integer characteristics,bilevel programming theory is utilized to structure the model.The model is solved using a mixed-integer particle swarmoptimization algorithm(MIPSO)to determine the optimal location and capacity of DG and ESS integrated into the distribution network to achieve the best economic benefits and operation quality.The proposed bilevel planning method for distribution networks is validated through simulations on the modified IEEE 33-bus system.The results demonstrate significant improvements,with the proposedmethod reducing the annual comprehensive cost by 41.65%and 13.98%,respectively,compared to scenarios without DG and ESS or with only DG integration.Furthermore,it reduces the daily average voltage deviation by 24.35%and 10.24%and daily network losses by 55.72%and 35.71%.
基金supported in part by the National Natural Science Foundation of China(51977127)in part by the ShanghaiMunicipal Science and in part by the Technology Commission(19020500800)“Shuguang Program”(20SG52)Shanghai Education Development Foundation and Shanghai Municipal Education Commission.
文摘As an effective carrier of integrated clean energy,the microgrid has attracted wide attention.The randomness of renewable energies such as wind and solar power output brings a significant cost and impact on the economics and reliability of microgrids.This paper proposes an optimization scheme based on the distributionally robust optimization(DRO)model for a microgrid considering solar-wind correlation.Firstly,scenarios of wind and solar power output scenarios are generated based on non-parametric kernel density estimation and the Frank-Copula function;then the generated scenario results are reduced by K-means clustering;finally,the probability confidence interval of scenario distribution is constrained by 1-norm and∞-norm.The model is solved by a column-and-constraint generation algorithm.Experimental studies are conducted on a microgrid system in Jiangsu,China and the obtained scheduling solution turned out to be superior under wind and solar power uncertainties,which verifies the effectiveness of the proposed DRO model.
文摘在风光等清洁能源渗透率及能源低碳化需求不断提高的背景下,如何精确模拟新能源出力不确定性及引导负荷侧柔性资源参与需求响应显得尤为重要。针对上述问题,本文提出一种计及源荷不确定性及阶梯型碳交易的虚拟电厂优化调度模型。首先,源侧基于Frank-Copula函数建立风光出力联合概率分布模型,采样约简得风光出力典型场景。其次,在多能耦合虚拟电厂中引入碳捕集与封存(carbon capture and storage,CCS)设备,降低碳排放,荷侧建立考虑柔性用户用能满意度的需求响应模型,以提升风光消纳。同时,引入阶梯型碳交易机制,建立源荷协同优化及低碳性改造的VPP日前优化调度模型。然后,采用梯形隶属度函数将多目标优化问题模糊化为单目标优化问题,调用CPLEX求解器求解。最后,通过算例分析验证本文方法的有效性。
