The intermittence and uncertainty of wind power pose challenges to large-scale wind power grid integration. The study of wind power uncertainty is becoming increasingly important for power system planning and operation. This paper proposes a wind power probabilistic interval prediction model, and a novel reliability assessment approach is presented for electrical power systems. First, the unknown parameters estimation of the autoregressive integrated moving average (ARIMA) prediction model is based on the Markov chain Monte Carlo (MCMC)-based Bayesian estimation method to improve the quality of statistical inference. Then, a quantum genetic algorithm is used to segment the power to determine the best output for each power segment weight and calculate the probabilistic prediction interval of wind power. Finally, reliability assessment by the sequential Monte Carlo simulation is presented combining with the probabilistic prediction interval of wind power on IEEE-RTS79 reliability test system. The simulation results that proposed variation range of reliability assessment indices consider the uncertain scenario of wind power and has guiding significance for power generation scheduling. Compared with genetic algorithm and particle swarm optimization algorithm, it is proved that the proposed prediction interval model has better prediction interval coverage probability index and interval average bandwidth index. INDEX TERMS Bayesian estimation, interval prediction, reliability index, sequential Monte Carlo method, wind power.

The intermittency and uncertainty of wind power result in challenges for large-scale wind power integration. Accurate wind power prediction is becoming increasingly important for power system planning and operation. In this paper, a probabilistic interval prediction method for wind power based on deep learning and particle swarm optimization (PSO) is proposed. Variational mode decomposition (VMD) and phase space reconstruction are used to pre-process the original wind power data to obtain additional details and uncover hidden information in the data. Subsequently, a bi-level convolutional neural network is used to learn nonlinear features in the pre-processed wind power data for wind power forecasting. PSO is used to determine the uncertainty of the point-based wind power prediction and to obtain the probabilistic prediction interval of the wind power. Wind power data from a Chinese wind farm and modeled wind power data provided by the United States Renewable Energy Laboratory are used to conduct extensive tests of the proposed method. The results show that the proposed method has competitive advantages for the point-based and probabilistic interval prediction of wind power.

Combined Cold Heat and Power (CCHP) is of great advantage in energy cascade using, but the energy grade brings more difficulties for energy equal transformation. Considering the rising of energy transformation efficiency, and the coefficient is decreasing which power converts into coal, so the transformation of different energy will change with production status. So we set out to research the problem from the angle of pollutant emission based on heat equivalent (HE), and proposed the principle of equal emission (PEE) that can reflect the dynamic transformation between cold heat and power, and establish the dynamic transformation model of different energy. Thereafter, the model of multi-objective economic dispatch is formulated including generation cost and environmental cost of CCHP based on the PEE, and then we solve the problem with the fuzzy optimization programming. The results show that PEE is important in economic dispatch and reducing emission, the more important is that it can reflect the change of energy production more accurately; it also provides the reference for the promotion of environmental dispatch of CCHP. Index Terms--CombinedCold Heat and Power, Multi-objective optimization, Energy grade, principle of equal emission.T

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