With the development of intelligent distribution networks and access to distributed energy, the solving the problem of timely and accurate determination of the operating state of the distribution network is an urgent task. Based on an improved analysis of the principle components of the network and statements of a self-organizing neural network, this article proposes the method to evaluate the operating state of medium- and low-voltage distribution networks. At the first step, the system of evaluating indices of the network is formed by advanced component analysis. The evaluation system is grounded on four aspects, including safety, reliability, quality and economy. Next, the self-organizing neural network is used to identify and clean up the data regarding the operating state of the distribution network. At the next step, the indicators are modeled at all levels; the entropy method is applied to calculate the total weight of all indicators. Then the value of each indicator is found and the weak links of the distribution network are determined. At the final stage, the comprehensive assessment of the real operation of the distribution network in Guangxi province is carried out. As shown, the method can effectively reduce the effect of abnormal data and subjectivity factor on the results of evaluating the state of the distribution network. That confirms the feasibility and practicability of the proposed method. References 22, figures 6, tables 6.

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The problem of wind power abandonment in the north areas of China is serious, which is mainly caused by the uncertainty of wind power and the "thermoelectric coupling" characteristics of cogeneration units. Aiming at this problem, this paper establishes the optimal scheduling model of generation-load-storage coordination in the electrothermal joint system with the goal of minimizing the amount of wind power discarded and the total scheduling cost of the system. Firstly, the responsive electrical and thermal loads on the demand side are divided into three types according to their types, namely, translatable, transferrable and reducible, and the demand response models are constructed to improve the fineness of the optimization on the demand side. Then, the scenario method is used to solve the uncertainty problem of wind power. Meanwhile, the influence of the energy storage side on system scheduling is considered. Finally, the results based on examples verify the effectiveness of the proposed model in promoting the absorptive of wind power and improving the overall economic benefit of the system. References 23, figures 8, table 1.

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