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The uncertainties of renewable energy may bring impacts to power system reliability. This paper proposes a novel generation rescheduling algorithm, which adjusts generation outputs to mitigate the variations of branch power flows and relieve the overload probability. This algorithm helps prevent power congestion and balance the aggregate renewable generations and loads in power systems. Besides, the paper also proposes a probabilistic method evaluating the system uncertainty issue in the power system considering generation rescheduling operation. The proposed generation rescheduling algorithm has been tested on the Arizona transmission system. The generation rescheduling method enhances power system reliability analysis with a high penetration of renewable energy resources.

The uncertainties of renewable energy may bring impacts to power system reliability. This paper proposes a novel generation rescheduling algorithm, which adjusts generation outputs to mitigate the variations of branch power flows and relieve the overload probability. This algorithm helps prevent power congestion and balance the aggregate renewable generations and loads in power systems. Besides, the paper also proposes a probabilistic method evaluating the system uncertainty issue in the power system considering generation rescheduling operation. The proposed generation rescheduling algorithm has been tested on the Arizona transmission system. The generation rescheduling method enhances power system reliability analysis with a high penetration of renewable energy resources.

This paper proposes an online steady-state voltage stability assessment scheme to evaluate the proximity to voltage collapse at each bus of a load area. Using a non-iterative holomorphic embedding method (HEM) with a proposed physical germ solution, an accurate loading limit at each load bus can be calculated based on online state estimation on the entire load area and a measurement-based equivalent for the external system. The HEM employs a power series to calculate an accurate Power-Voltage (P-V) curve at each load bus and accordingly evaluates the voltage stability margin considering load variations in the next period. An adaptive two-stage Pade approximants method is proposed to improve the convergence of the power series for accurate determination of the nose point on the P-V curve with moderate computational burden. The proposed method is illustrated in detail on a 4-bus test system and then demonstrated on a load area of the Northeast Power Coordinating Council (NPCC) 48-geneartor, 140-bus power system. Revised and Submitted to IEEE Transaction on Power Systems

This paper proposes an online steady-state voltage stability assessment scheme to evaluate the proximity to voltage collapse at each bus of a load area. Using a non-iterative holomorphic embedding method (HEM) with a proposed physical germ solution, an accurate loading limit at each load bus can be calculated based on online state estimation on the entire load area and a measurement-based equivalent for the external system. The HEM employs a power series to calculate an accurate Power-Voltage (P-V) curve at each load bus and accordingly evaluates the voltage stability margin considering load variations in the next period. An adaptive two-stage Pade approximants method is proposed to improve the convergence of the power series for accurate determination of the nose point on the P-V curve with moderate computational burden. The proposed method is illustrated in detail on a 4-bus test system and then demonstrated on a load area of the Northeast Power Coordinating Council (NPCC) 48-geneartor, 140-bus power system. Revised and Submitted to IEEE Transaction on Power Systems