为科学揭示梯级水库群运行对河流生态的影响,基于黄河上游实测水沙序列,采用IHA(Indicators of Hydrologic Alteration)指标体系,对比分析了不同工程运行时期黄河上游水文情势变化,运用多系列贡献率分割法,量化了不同影响因子对水文情...为科学揭示梯级水库群运行对河流生态的影响,基于黄河上游实测水沙序列,采用IHA(Indicators of Hydrologic Alteration)指标体系,对比分析了不同工程运行时期黄河上游水文情势变化,运用多系列贡献率分割法,量化了不同影响因子对水文情势变化的贡献率。通过输沙率法结合断面淤积形态分析揭示了黄河上游河道冲淤演变。结果表明,黄河上游水库运行对河流径流及河道形态产生了深刻影响,进而影响了河流生态。水库运行后非汛期月均流量上升、汛期月均流量下降、高流量事件发生频率与流量减少,径流趋于平缓,且宁蒙河段泥沙淤积、断面形态趋于宽浅。分析表明水库运行是造成黄河上游兰州水文情势变化的主要原因,以及石嘴山、头道拐水文情势变化的重要原因,高流量事件的减少加剧了河道淤积,使河流生态朝不利方向演化,为维护黄河上游生态健康有必要实施生态调度,提高涨水期和洪水期下泄流量并制造高流量事件。研究为评估梯级水库运行的生态影响、指导梯级水库生态调度提供方向性参考。展开更多
For the planning,operation and control of multiterminal voltage source converter(VSC)based high-voltage direct current(HVDC)(VSC-MTDC)systems,an accurate power flow formulation is a key starting point.Conventional pow...For the planning,operation and control of multiterminal voltage source converter(VSC)based high-voltage direct current(HVDC)(VSC-MTDC)systems,an accurate power flow formulation is a key starting point.Conventional power flow formulations assume the constant frequencies for all asynchronous AC systems.Therefore,a new feature about the complex coupling relations between AC frequencies,DC voltages and the exchanged power via VSC stations cannot be characterized if VSC-MTDC systems are required to provide cross-regional frequency responses.To address this issue,this paper proposes a comprehensive frequency-dependent power flow formulation.The proposed approach takes the frequencies of asynchronous AC systems as explicit variables,and investigates the novel bus models of the interlinking buses of VSC stations.The proposed approach accommodates different operation modes and frequency droop strategies of VSC stations,and considers the power losses of VSC stations.The effectiveness and generality of the developed approach are validated by a 6-terminal VSC-HVDC test system.The test system presents the characteristics of the coexistence of numerous VSC operation modes,the absence of slack buses in both AC and DC subsystems,and diversified grid configurations such as point-to-point integration of renewable energy sources and one AC system integrated with multiple VSC stations.展开更多
With the increasing complexity of power system structures and the increasing penetration of renewable energy,the number of possible power system operation modes increases dramatically.It is difficult to make manual po...With the increasing complexity of power system structures and the increasing penetration of renewable energy,the number of possible power system operation modes increases dramatically.It is difficult to make manual power flow adjustments to establish an initial convergent power flow that is suitable for operation mode analysis.At present,problems of low efficiency and long time consumption are encountered in the formulation of operation modes,resulting in a very limited number of generated operation modes.In this paper,we propose an intelligent power flow adjustment and generation model based on a deep network and reinforcement learning.First,a discriminator is trained to judge the power flow convergence,and the output of this discriminator is used to construct a value function.Then,the reinforcement learning method is adopted to learn a strategy for power flow convergence adjustment.Finally,a large number of convergent power flow samples are generated using the learned adjustment strategy.Compared with the traditional flow adjustment method,the proposed method has significant advantages that the learning of the power flow adjustment strategy does not depend on the parameters of the power system model.Therefore,this strategy can be automatically learned without manual intervention,which allows a large number of different operation modes to be efficiently formulated.The verification results of a case study show that the proposed method can independently learn a power flow adjustment strategy and generate various convergent power flows.展开更多
文摘为科学揭示梯级水库群运行对河流生态的影响,基于黄河上游实测水沙序列,采用IHA(Indicators of Hydrologic Alteration)指标体系,对比分析了不同工程运行时期黄河上游水文情势变化,运用多系列贡献率分割法,量化了不同影响因子对水文情势变化的贡献率。通过输沙率法结合断面淤积形态分析揭示了黄河上游河道冲淤演变。结果表明,黄河上游水库运行对河流径流及河道形态产生了深刻影响,进而影响了河流生态。水库运行后非汛期月均流量上升、汛期月均流量下降、高流量事件发生频率与流量减少,径流趋于平缓,且宁蒙河段泥沙淤积、断面形态趋于宽浅。分析表明水库运行是造成黄河上游兰州水文情势变化的主要原因,以及石嘴山、头道拐水文情势变化的重要原因,高流量事件的减少加剧了河道淤积,使河流生态朝不利方向演化,为维护黄河上游生态健康有必要实施生态调度,提高涨水期和洪水期下泄流量并制造高流量事件。研究为评估梯级水库运行的生态影响、指导梯级水库生态调度提供方向性参考。
基金supported by the National Key Research and Development Program of China(No.2017YFB0902200)National Natural Science Foundation of China(No.U1766201)State Grid Technology Project(No.SGGSKY00FJJS1600209)。
文摘For the planning,operation and control of multiterminal voltage source converter(VSC)based high-voltage direct current(HVDC)(VSC-MTDC)systems,an accurate power flow formulation is a key starting point.Conventional power flow formulations assume the constant frequencies for all asynchronous AC systems.Therefore,a new feature about the complex coupling relations between AC frequencies,DC voltages and the exchanged power via VSC stations cannot be characterized if VSC-MTDC systems are required to provide cross-regional frequency responses.To address this issue,this paper proposes a comprehensive frequency-dependent power flow formulation.The proposed approach takes the frequencies of asynchronous AC systems as explicit variables,and investigates the novel bus models of the interlinking buses of VSC stations.The proposed approach accommodates different operation modes and frequency droop strategies of VSC stations,and considers the power losses of VSC stations.The effectiveness and generality of the developed approach are validated by a 6-terminal VSC-HVDC test system.The test system presents the characteristics of the coexistence of numerous VSC operation modes,the absence of slack buses in both AC and DC subsystems,and diversified grid configurations such as point-to-point integration of renewable energy sources and one AC system integrated with multiple VSC stations.
基金supported by the Science and Technology Project of the State Grid Corporation of China(No.5400-201935258A-0-0-00)the National Natural Science Foundation of China(No.51777104)
文摘With the increasing complexity of power system structures and the increasing penetration of renewable energy,the number of possible power system operation modes increases dramatically.It is difficult to make manual power flow adjustments to establish an initial convergent power flow that is suitable for operation mode analysis.At present,problems of low efficiency and long time consumption are encountered in the formulation of operation modes,resulting in a very limited number of generated operation modes.In this paper,we propose an intelligent power flow adjustment and generation model based on a deep network and reinforcement learning.First,a discriminator is trained to judge the power flow convergence,and the output of this discriminator is used to construct a value function.Then,the reinforcement learning method is adopted to learn a strategy for power flow convergence adjustment.Finally,a large number of convergent power flow samples are generated using the learned adjustment strategy.Compared with the traditional flow adjustment method,the proposed method has significant advantages that the learning of the power flow adjustment strategy does not depend on the parameters of the power system model.Therefore,this strategy can be automatically learned without manual intervention,which allows a large number of different operation modes to be efficiently formulated.The verification results of a case study show that the proposed method can independently learn a power flow adjustment strategy and generate various convergent power flows.
