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For an overhead conductor, meteorological correlations exist among the meteorological elements that dominantly determine its thermal rating, and temporal correlations exist among the thermal ratings in sequential time periods. It is necessary to exploit these correlations to improve the performance of the probabilistic prediction of thermal ratings. To this end, a copula-based method of joint probability density prediction for multiperiod thermal ratings (JPDP-MPTR) is presented in this paper. In this method, the probability density functions (PDFs) of the thermal ratings for every 15 minutes over a 1-hour horizon are first predicted individually, considering the correlations among meteorological elements. Then, the joint probability density function (JPDF) of the multiperiod thermal ratings is further formulated based on copula theory. Finally, the probability distributions of the thermal ratings in the predicted time periods are estimated via joint sampling based on the JPDF. Numerical simulations based on actual meteorological data collected around an overhead conductor show that the proposed method can significantly improve prediction results through the integration of meteorological and temporal correlations into the probabilistic prediction of the thermal rating.
Xu Jin; Mengxia Wang; Mingjian Cui; Hua Sun; Ming Yang. Joint Probability Density Prediction for Multiperiod Thermal Ratings of Overhead Conductors. IEEE Transactions on Power Delivery 2020, PP, 1 -1.
AMA StyleXu Jin, Mengxia Wang, Mingjian Cui, Hua Sun, Ming Yang. Joint Probability Density Prediction for Multiperiod Thermal Ratings of Overhead Conductors. IEEE Transactions on Power Delivery. 2020; PP (99):1-1.
Chicago/Turabian StyleXu Jin; Mengxia Wang; Mingjian Cui; Hua Sun; Ming Yang. 2020. "Joint Probability Density Prediction for Multiperiod Thermal Ratings of Overhead Conductors." IEEE Transactions on Power Delivery PP, no. 99: 1-1.
This paper proposes a Lyapunov optimization-based online distributed (LOOD) algorithmic framework for active distribution networks with numerous photovoltaic inverters and invert air conditionings (IACs). In the proposed scheme, ADNs can track an active power setpoint reference at the substation in response to transmission-level requests while concurrently minimizing the utility loss and ensuring the security of voltages. In contrast to conventional distributed optimization methods that employ the setpoints for controllable devices only when the algorithm converges, the proposed LOOD can carry out the setpoints immediately relying on the current measurements and operation conditions. Notably, the time-coupling constraints of IACs are decoupled for online implementation with Lyapunov optimization technique. An incentive scheme is tailored to coordinate the customer-owned assets in lieu of the direct control from network operators. Optimality and convergency are characterized analytically. Finally, we corroborate the proposed method on a modified version of 33-node test feeder.
Shuai Fan; Guangyu He; Xinyang Zhou; Mingjian Cui. Online Optimization for Networked Distributed Energy Resources with Time-Coupling Constraints. 2019, 1 .
AMA StyleShuai Fan, Guangyu He, Xinyang Zhou, Mingjian Cui. Online Optimization for Networked Distributed Energy Resources with Time-Coupling Constraints. . 2019; ():1.
Chicago/Turabian StyleShuai Fan; Guangyu He; Xinyang Zhou; Mingjian Cui. 2019. "Online Optimization for Networked Distributed Energy Resources with Time-Coupling Constraints." , no. : 1.
This paper proposes a Lyapunov optimization-based online distributed (LOOD) algorithmic framework for active distribution networks with numerous photovoltaic inverters and invert air conditionings (IACs). In the proposed scheme, ADNs can track an active power setpoint reference at the substation in response to transmission-level requests while concurrently minimizing the utility loss and ensuring the security of voltages. In contrast to conventional distributed optimization methods that employ the setpoints for controllable devices only when the algorithm converges, the proposed LOOD can carry out the setpoints immediately relying on the current measurements and operation conditions. Notably, the time-coupling constraints of IACs are decoupled for online implementation with Lyapunov optimization technique. An incentive scheme is tailored to coordinate the customer-owned assets in lieu of the direct control from network operators. Optimality and convergency are characterized analytically. Finally, we corroborate the proposed method on a modified version of 33-node test feeder.
Shuai Fan; Guangyu He; Xinyang Zhou; Mingjian Cui. Online Optimization for Networked Distributed Energy Resources with Time-Coupling Constraints. 2019, 1 .
AMA StyleShuai Fan, Guangyu He, Xinyang Zhou, Mingjian Cui. Online Optimization for Networked Distributed Energy Resources with Time-Coupling Constraints. . 2019; ():1.
Chicago/Turabian StyleShuai Fan; Guangyu He; Xinyang Zhou; Mingjian Cui. 2019. "Online Optimization for Networked Distributed Energy Resources with Time-Coupling Constraints." , no. : 1.
The cybersecurity of wind farms is an increasing concern in recent years, and its impacts on the power system reliability have not been fully studied. In this paper, the pressing issues of wind farms, including cybersecurity and wind power ramping events (WPRs) are incorporated into a new reliability evaluation approach. Cyber–physical failures like the instantaneous failure and longtime fatigue of wind turbines are considered in the reliability evaluation. The tripping attack is modeled in a bilevel optimal power flow model which aims to maximize the load shedding on the system’s vulnerable moment. The time-varying failure rate of wind turbine is approximated by Weibull distribution which incorporates the service time and remaining life of wind turbine. Various system defense capacities and penetration rates of wind power are simulated on the typical reliability test system. The comparative and sensitive analyses show that power system reliability is challenged by the cybersecurity of wind farms, especially when the installed capacity of wind power continues to rise. The timely patching of network vulnerabilities and the life management of wind turbines are important measures to ensure the cyber–physical security of wind farms.
Honghao Wu; Junyong Liu; Jichun Liu; Mingjian Cui; Xuan Liu; Hongjun Gao. Power Grid Reliability Evaluation Considering Wind Farm Cyber Security and Ramping Events. Applied Sciences 2019, 9, 3003 .
AMA StyleHonghao Wu, Junyong Liu, Jichun Liu, Mingjian Cui, Xuan Liu, Hongjun Gao. Power Grid Reliability Evaluation Considering Wind Farm Cyber Security and Ramping Events. Applied Sciences. 2019; 9 (15):3003.
Chicago/Turabian StyleHonghao Wu; Junyong Liu; Jichun Liu; Mingjian Cui; Xuan Liu; Hongjun Gao. 2019. "Power Grid Reliability Evaluation Considering Wind Farm Cyber Security and Ramping Events." Applied Sciences 9, no. 15: 3003.