To improve the operation efficiency of the photovoltaic power station complementary power generation system,an optimal allocation model of the photovoltaic power station complementary power generation capacity based o...To improve the operation efficiency of the photovoltaic power station complementary power generation system,an optimal allocation model of the photovoltaic power station complementary power generation capacity based on PSO-BP is proposed.Particle Swarm Optimization and BP neural network are used to establish the forecasting model,the Markov chain model is used to correct the forecasting error of the model,and the weighted fitting method is used to forecast the annual load curve,to complete the optimal allocation of complementary generating capacity of photovoltaic power stations.The experimental results show that thismethod reduces the average loss of photovoltaic output prediction,improves the prediction accuracy and recall rate of photovoltaic output prediction,and ensures the effective operation of the power system.展开更多
With the rapid growth of photovoltaic integration,the volatility and uncertainty of intermittent photovoltaic injection will dramatically reduce system operation reliability from the generation side.The system operato...With the rapid growth of photovoltaic integration,the volatility and uncertainty of intermittent photovoltaic injection will dramatically reduce system operation reliability from the generation side.The system operator may face certain financial risks brought by unexpected power failure under low operation reliability.Therefore,maintaining sufficient power reserve to meet system operation reliability and reduce risk,especially in an isolated system,is essential.However,the traditional reserve preparation strategy fails to consider the uncertainties of the power generation under the high penetration levels of emerging renewable energy resources.A novel reserve preparation strategy for an isolated system is developed in this paper using a twostage model.In the first stage,the optimal hourly scheduling of an isolated system is determined.In the second stage,a minute level conditional value-at-risk(CVaR)based model is established where the uncertainty of the reserve requirement is introduced with the chance constraint.The proposed discretized step transformation(DST)and subtraction type convolution(STC)methods are utilized to convert the model into mixedinteger linear programming,and finally solved by applying the CPLEX solver.The IEEE 39-bus system is used as the test case to validate the feasibility and effectiveness of the proposed two-stage model.展开更多
文摘To improve the operation efficiency of the photovoltaic power station complementary power generation system,an optimal allocation model of the photovoltaic power station complementary power generation capacity based on PSO-BP is proposed.Particle Swarm Optimization and BP neural network are used to establish the forecasting model,the Markov chain model is used to correct the forecasting error of the model,and the weighted fitting method is used to forecast the annual load curve,to complete the optimal allocation of complementary generating capacity of photovoltaic power stations.The experimental results show that thismethod reduces the average loss of photovoltaic output prediction,improves the prediction accuracy and recall rate of photovoltaic output prediction,and ensures the effective operation of the power system.
文摘With the rapid growth of photovoltaic integration,the volatility and uncertainty of intermittent photovoltaic injection will dramatically reduce system operation reliability from the generation side.The system operator may face certain financial risks brought by unexpected power failure under low operation reliability.Therefore,maintaining sufficient power reserve to meet system operation reliability and reduce risk,especially in an isolated system,is essential.However,the traditional reserve preparation strategy fails to consider the uncertainties of the power generation under the high penetration levels of emerging renewable energy resources.A novel reserve preparation strategy for an isolated system is developed in this paper using a twostage model.In the first stage,the optimal hourly scheduling of an isolated system is determined.In the second stage,a minute level conditional value-at-risk(CVaR)based model is established where the uncertainty of the reserve requirement is introduced with the chance constraint.The proposed discretized step transformation(DST)and subtraction type convolution(STC)methods are utilized to convert the model into mixedinteger linear programming,and finally solved by applying the CPLEX solver.The IEEE 39-bus system is used as the test case to validate the feasibility and effectiveness of the proposed two-stage model.
