To address uncertainty as well as transient stability constraints simultaneously in the preventive control of windfarm systems, a novel three-stage optimization strategy is established in this paper. In the first stag...To address uncertainty as well as transient stability constraints simultaneously in the preventive control of windfarm systems, a novel three-stage optimization strategy is established in this paper. In the first stage, the probabilisticmulti-objective particle swarm optimization based on the point estimate method is employed to cope with thestochastic factors. The transient security region of the system is accurately ensured by the interior point methodin the second stage. Finally, the verification of the final optimal objectives and satisfied constraints are enforcedin the last stage. Furthermore, the proposed strategy is a general framework that can combine other optimizationalgorithms. The proposed methodology is tested on the modified WSCC 9-bus system and the New England 39-bussystem. The results verify the feasibility of the method.展开更多
Transient stability-constrained optimal power flow(TSCOPF)optimizes power flow while ensuring stability againstpotential contingencies.The main challenges faced by TSCOPFare the handling of transient constraints.The d...Transient stability-constrained optimal power flow(TSCOPF)optimizes power flow while ensuring stability againstpotential contingencies.The main challenges faced by TSCOPFare the handling of transient constraints.The differential equations of the dynamic process and the inherent nonlinearityof power systems make modeling and solving them difficult.Currently,four methods are employed in TSCOPF:discretizationmethods,sequential methods,heuristic methods,and machinelearning methods.The paper summarizes the latest literaturereview of these methods,including transient stability criteria,handling of stability constraints,computation flowcharts,advantages,and limitations.Moreover,as power systems rapidlyadvance,the discussion focuses on how they adapt to newelements and challenges.In conclusion,the existing research gapand future directions are presented.展开更多
文摘To address uncertainty as well as transient stability constraints simultaneously in the preventive control of windfarm systems, a novel three-stage optimization strategy is established in this paper. In the first stage, the probabilisticmulti-objective particle swarm optimization based on the point estimate method is employed to cope with thestochastic factors. The transient security region of the system is accurately ensured by the interior point methodin the second stage. Finally, the verification of the final optimal objectives and satisfied constraints are enforcedin the last stage. Furthermore, the proposed strategy is a general framework that can combine other optimizationalgorithms. The proposed methodology is tested on the modified WSCC 9-bus system and the New England 39-bussystem. The results verify the feasibility of the method.
基金supported by Ministry of Education(MOE),Republic of Singapore,under grant AcRF TIER-1 RT11/22supported by the JC STEM Lab of Future Energy Systems(2025-0039)+1 种基金the Global STEM Professorship(GSP313)a Startup Grant from the City University of Hong Kong.
文摘Transient stability-constrained optimal power flow(TSCOPF)optimizes power flow while ensuring stability againstpotential contingencies.The main challenges faced by TSCOPFare the handling of transient constraints.The differential equations of the dynamic process and the inherent nonlinearityof power systems make modeling and solving them difficult.Currently,four methods are employed in TSCOPF:discretizationmethods,sequential methods,heuristic methods,and machinelearning methods.The paper summarizes the latest literaturereview of these methods,including transient stability criteria,handling of stability constraints,computation flowcharts,advantages,and limitations.Moreover,as power systems rapidlyadvance,the discussion focuses on how they adapt to newelements and challenges.In conclusion,the existing research gapand future directions are presented.
