This paper presents the Interplanetary Magnetic Field (IMF) observations at 0.72 AU measured by Venus Express (VEX) and 1 AU by Advanced Composition Explorer (ACE) in 2007.The distributions of daily averages of B are ...This paper presents the Interplanetary Magnetic Field (IMF) observations at 0.72 AU measured by Venus Express (VEX) and 1 AU by Advanced Composition Explorer (ACE) in 2007.The distributions of daily averages of B are lognormal in both locations.The multiscale structure of the magnetic field fluctuations was described by studying the increments of B over a range scales from 10 min to 21.3 hours.All the Probability Distribution Functions (PDFs) can be described quantitatively by Tsallis distribution function.On the ecliptic plane from 0.72 AU to 1 AU,the entropy index q increases with distance over all scales,indicating the intermittency of turbulence is growing.The widths of the PDFs at 0.72 AU are larger than those at 1 AU at all scales,which indicating the turbulence at 0.72 AU is more intense than that at 1 AU.This helps us understand the nature and development of the magnetic field fluctuations.展开更多
针对电池储能(battery energy storage system,BESS)平抑风电波动过程中电池单元荷电状态(state of charge,SOC)均衡性较差且未考虑风储净收益的问题,提出了风电波动平抑下考虑SOC均衡及收益的BESS功率分配策略。首先,建立综合考虑售电...针对电池储能(battery energy storage system,BESS)平抑风电波动过程中电池单元荷电状态(state of charge,SOC)均衡性较差且未考虑风储净收益的问题,提出了风电波动平抑下考虑SOC均衡及收益的BESS功率分配策略。首先,建立综合考虑售电收益、弃风惩罚、缺电惩罚及BESS运行成本等多个因素的风电并网指令优化模型,以并网指令波动率、电池组SOC标准差等多个因素为约束条件,提出改进算术优化算法(improved arithmetic optimization algorithm,IAOA)求解该优化模型。然后,将BESS划分为两个电池组,设计了BESS双层功率分配方法(double-layer power allocation method,DPAM),上层将BESS充放电指令分配给两个电池组,下层根据最大充放电功率原则或新型SOC均衡原则将电池组充放电指令分配给各自的电池单元。最后,通过仿真对所提策略进行了验证。仿真结果表明:IAOA加快了寻优速度,提高了寻优精度;DPAM提升了电池组内电池单元SOC的均衡速度,改善了均衡程度;提出的功率分配策略进一步降低了风电并网波动率,同时提高了风储系统净收益。展开更多
基金Supported by the NNSFC (40921063) and CAS grant KJCX2-YW-T13
文摘This paper presents the Interplanetary Magnetic Field (IMF) observations at 0.72 AU measured by Venus Express (VEX) and 1 AU by Advanced Composition Explorer (ACE) in 2007.The distributions of daily averages of B are lognormal in both locations.The multiscale structure of the magnetic field fluctuations was described by studying the increments of B over a range scales from 10 min to 21.3 hours.All the Probability Distribution Functions (PDFs) can be described quantitatively by Tsallis distribution function.On the ecliptic plane from 0.72 AU to 1 AU,the entropy index q increases with distance over all scales,indicating the intermittency of turbulence is growing.The widths of the PDFs at 0.72 AU are larger than those at 1 AU at all scales,which indicating the turbulence at 0.72 AU is more intense than that at 1 AU.This helps us understand the nature and development of the magnetic field fluctuations.
文摘风电特有的间歇性和波动性,影响电网稳定运行。为减小风电波动对电网的影响,该文以平抑风电功率波动为目标,构建基于多步模型算法控制(model algorithm control,MAC)的混合储能平抑–定容双层规划模型。上层模型以储能最小出力和储能充放平衡为目标函数,采用MAC算法求解出混合储能总作用域;然后,提出考虑混合储能经济性的自适应滑动窗口调节方法,通过滑动平均滤波(moving average filter,MAF)将总作用域分解为蓄电池作用域和超级电容器作用域,使超级电容器作用于控制序列变化率较大的部分,蓄电池作用于控制序列的平滑部分。基于MAC-MAF作用域制定了储能运行策略。根据上层求解结果和储能运行策略建立了下层超级电容器和蓄电池容量最优配比模型,该模型以混合储能系统日均运行成本最低和最大化平抑风电波动为目标函数,采用多目标哈里斯鹰算法求解上述模型。以新疆某50MW风电场验证了所提策略的合理性及模型求解方法的有效性。
文摘针对电池储能(battery energy storage system,BESS)平抑风电波动过程中电池单元荷电状态(state of charge,SOC)均衡性较差且未考虑风储净收益的问题,提出了风电波动平抑下考虑SOC均衡及收益的BESS功率分配策略。首先,建立综合考虑售电收益、弃风惩罚、缺电惩罚及BESS运行成本等多个因素的风电并网指令优化模型,以并网指令波动率、电池组SOC标准差等多个因素为约束条件,提出改进算术优化算法(improved arithmetic optimization algorithm,IAOA)求解该优化模型。然后,将BESS划分为两个电池组,设计了BESS双层功率分配方法(double-layer power allocation method,DPAM),上层将BESS充放电指令分配给两个电池组,下层根据最大充放电功率原则或新型SOC均衡原则将电池组充放电指令分配给各自的电池单元。最后,通过仿真对所提策略进行了验证。仿真结果表明:IAOA加快了寻优速度,提高了寻优精度;DPAM提升了电池组内电池单元SOC的均衡速度,改善了均衡程度;提出的功率分配策略进一步降低了风电并网波动率,同时提高了风储系统净收益。
