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Because wind power spillage is barely considered, the existing robust unit commitment cannot accurately analyze the impacts of wind power accommodation on on/off schedules and spinning reserve requirements of conventional generators and cannot consider the network security limits. In this regard, a novel double-level robust security-constrained unit commitment formulation with optimizable interval of uncertain wind power output is firstly proposed in this paper to obtain allowable interval solutions for wind power generation and provide the optimal schedules for conventional generators to cope with the uncertainty in wind power generation. The proposed double-level model is difficult to be solved because of the invalid dual transform in solution process caused by the coupling relation between the discrete and continuous variables. Therefore, a two-stage iterative solution method based on Benders Decomposition is also presented. The proposed double-level model is transformed into a single-level and twostage robust interval unit commitment model by eliminating the coupling relation, and then this two-stage model can be solved by Benders Decomposition iteratively. Simulation studies on a modified IEEE 26-generator reliability test system connected to a wind farm are conducted to verify the effectiveness and advantages of the proposed model and solution method.

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This paper proposes a novel security-constrained unit commitment model to calculate the optimal spinning reserve (SR) amount. The model combines cost-benefit analysis with an improved multiscenario risk analysis method capable of considering various uncertainties, including load and wind power forecast errors as well as forced outages of generators. In this model, cost-benefit analysis is utilized to simultaneously minimize the operation cost of conventional generators, the expected cost of load shedding, the penalty cost of wind power spillage, and the carbon emission cost. It remedies the defects of the deterministic and probabilistic methods of SR calculation. In cases where load and wind power generation are negatively correlated, this model based on multistep modeling of net demand can consider the wind power curtailment to maximize the overall economic efficiency of system operation so that the optimal economic values of wind power and SR are achieved. In addition, the impact of the nonnormal probability distributions of wind power forecast error on SR optimization can be taken into account. Using mixed integer linear programming method, simulation studies on a modified IEEE 26-generator reliability test system connected to a wind farm are performed to confirm the effectiveness and advantage of the proposed model.

Controlled islanding is considered to be the last countermeasure to prevent system-wide blackouts in case of cascading failures. It splits the system into self-sustained islands to maintain transient stability at the expense of possible loss of load. Generator coherence identification is critical to controlled islanding scheme as it helps identify the optimal cutset to maintain system transient stability. This paper presents a novel approach for online generator coherency identification using phasor measurement unit (PMU) data and dynamic time warping (DTW). Results from the coherence identification are used to further cluster non-generator buses using spectral clustering with the objective of minimizing power flow disruption. The proposed approach is validated and compared to existing methods on the IEEE 39-bus system, through which its advantages are demonstrated.