This paper presents a finite-time economic model predictive control(MPC)algorithm that can be used for frequency regulation and optimal load dispatch in multi-area power systems.Economic MPC can be used in a power sys...This paper presents a finite-time economic model predictive control(MPC)algorithm that can be used for frequency regulation and optimal load dispatch in multi-area power systems.Economic MPC can be used in a power system to ensure frequency stability,real-time economic optimization,control of the system and optimal load dispatch from it.A generalized terminal penalty term was used,and the finite-time convergence of the system was guaranteed.The effectiveness of the proposed model predictive control algorithm was verified by simulating a power system,which had two areas connected by an AC tie line.The simulation results demonstrated the effectiveness of the algorithm.展开更多
With the increasing number of wind farms in power systems, the scheduling of a single wind farm needs to be improved. For this end, this paper proposes an optimal short-term load dispatch strategy for a single wind fa...With the increasing number of wind farms in power systems, the scheduling of a single wind farm needs to be improved. For this end, this paper proposes an optimal short-term load dispatch strategy for a single wind farm. Firstly, considering the large number of wind units and the high dimensionality of the scheduling solutions, we analyze the unit load characteristics, from which we extract the unit load characteristic matrix, and then classify the wind power units with the FCM fuzzy clustering algorithm. Secondly, we define the running loss indicator and action loss indicator. Based on the prediction of wind power and the load instructions, we establish a unit commitment model in wind farm, and solve the model using a combination of the fuzzy clustering algorithm and genetic algorithm, which overcomes the difficulty of the high dimensionality of the solution in the wind farm scheduling problem, to obtain the optimal scheduling strategy. Finally, through the simulation of the scheduling strategy for a 45 MW wind farm, we demonstrate the feasibility and effectiveness of the proposed strategy.展开更多
基金supported by the National Natural Science Foundation of China(Grant 62103101)the Natural Science Foundation of Jiangsu Province of China(Grant BK20210217)+5 种基金the China Postdoctoral Science Foundation(Grant 2022M710680)the National Natural Science Foundation of China(Grant 62273094)the"Zhishan"Scholars Programs of Southeast Universitythe Fundamental Science(Natural Science)General Program of Jiangsu Higher Education Institutions(No.21KJB470020)the Open Research Fund of Jiangsu Collaborative Innovation Center for Smart Distribution Network,Nanjing Institute of Technology(No.XTCX202102)the Introduced Talents Scientific Research Start-up Fund Project,Nanjing Institute of Technology(No.YKJ202133).
文摘This paper presents a finite-time economic model predictive control(MPC)algorithm that can be used for frequency regulation and optimal load dispatch in multi-area power systems.Economic MPC can be used in a power system to ensure frequency stability,real-time economic optimization,control of the system and optimal load dispatch from it.A generalized terminal penalty term was used,and the finite-time convergence of the system was guaranteed.The effectiveness of the proposed model predictive control algorithm was verified by simulating a power system,which had two areas connected by an AC tie line.The simulation results demonstrated the effectiveness of the algorithm.
基金supported by the National Basic Research Program of China ("973" Project) (Grant No. 2012CB215203)the National Natural Science Major Fund Project (Grant No. 51036002)
文摘With the increasing number of wind farms in power systems, the scheduling of a single wind farm needs to be improved. For this end, this paper proposes an optimal short-term load dispatch strategy for a single wind farm. Firstly, considering the large number of wind units and the high dimensionality of the scheduling solutions, we analyze the unit load characteristics, from which we extract the unit load characteristic matrix, and then classify the wind power units with the FCM fuzzy clustering algorithm. Secondly, we define the running loss indicator and action loss indicator. Based on the prediction of wind power and the load instructions, we establish a unit commitment model in wind farm, and solve the model using a combination of the fuzzy clustering algorithm and genetic algorithm, which overcomes the difficulty of the high dimensionality of the solution in the wind farm scheduling problem, to obtain the optimal scheduling strategy. Finally, through the simulation of the scheduling strategy for a 45 MW wind farm, we demonstrate the feasibility and effectiveness of the proposed strategy.