Abstract
The micro-grid concept has recently gained increasing attention for power system operators to increase the operational effectiveness and provide a more reliable, sustainable and economic power system. To ensure the autonomous power supply when disconnected from the bulk power system, energy storage system (ESS) options as well as demand side flexibility can be used to provide the local balance between time-varying production and consumption in a micro-grid structure. In this study, the demand side flexibility via thermostatically controllable appliances (TCAs)-based direct load control (DLC) strategies and common ESSs-based bi-directional energy flow possibility is considered for the economic operation of a micro-grid. Different case studies are conducted for validating the effectiveness of the proposed structure.

Abstract

Abstract
Due to the stochastic fluctuant characteristic of solar irradiance, large-scale grid-connected photovoltaic (PV) power plants can bring great difficulties to the operation of the power system. In order to fulfil the sky images based ultra-short term PV power forecasting and enhance the grid consumptive ability of PV power, an accurate model that can map sky images to corresponding surface solar irradiance is very significant. Therefore, in this paper a neural network based irradiance mapping model of solar PV power forecasting using sky image is proposed. First, we combine the theoretical calculation of extraterrestrial solar irradiance and atmospheric optical thickness to establish the clearance surface irradiance model. Second, the sky images observed by total sky imager are processed to extract image features related to solar irradiance. Third, a neural network based irradiance mapping model is built and trained using historical sky images and solar irradiance data. Simulation results show that the proposed model can map sky image features to surface solar irradiance accurately in different weather conditions. © 2018 IEEE

Abstract
The motion of cloud over photovoltaic (PV) power station will directly cause the change of solar irradiance, which indirectly affects the prediction of minute-level PV power, so the tracking of cloud motion is very crucial. In this study, Block-matching algorithm, Optical Flow algorithm and feature matching algorithm are three prevailing methods. However, as a rigid registration method, Block-matching cannot obtain the parameters of cloud deformation or rotation. The accuracy of the optical flow, which is based on the assumption that the image grayscale is not changed, is easily disturbed by noise. When the image texture information is not rich enough, the accuracy of the feature matching will also be reduced. That is, in order to improve their robustness, they must be combined through a certain strategy. Therefore, a pattern classification and PSO optimal weights based sky images cloud motion speed calculation method for solar PV power forecasting (PCPOW) is proposed in this paper. The method consists of two parts. Firstly, we use k-means clustering method and texture features based on Gray-Level Co-occurrence Matrix (GLCM) to classify the clouds. Because texture can adequately reflect image information, compared with other image features, it can better take into account both the macro nature and the fine structure of images. Secondly, for different cloud classes, we build the corresponding combined calculation modeling to obtain cloud motion speed. The Particle Swarm Optimization algorithm is used to give different weights to different methods to adapt to different clouds. The performances of the method are investigated using real data recorded at Yunnan Electric Power Research Institute. Under the measurement of common precision index, the comparisons with various benchmark methods show the effectiveness of the proposed approaches over cloud tracing. © 2018 IEEE

Abstract
In recent years, with the vigorous development of the new energy industry in the world, distributed photovoltaics (PV) have strongly penetrated the international energy markets at exponential growth rates, and a large number of electric vehicles (EVs) have been used mainly driven by policies. The use of EVs and distributed PV would lead to an increase in load uncertainty. Hence, a new day-ahead portfolio optimization model for a power supply company with distributed PV considering EVs was developed. The model contains risks depending on market price fluctuation and load uncertainty caused by EVs load, conventional load and distributed PV's output, considering the expected cost of errors, and helping to determine an optimal quantity of power to be obtained from distributed PV's output and different electricity markets. This paper analyses the efficient frontier of conditional value-at-risk (CVaR) and the influence of different EVs market penetration levels and distributed PV's output on the portfolio strategy. © 2018 IEEE

Abstract