An Improved Model of Total Supply Capability for Distribution Systems
The Total Supply Capability (TSC) model is newly proposed to evaluate the maximum load a distribution system can supply under the N-1 security guideline. This TSC model and a linear programming algorithm can calculate the accurate TSC of a distribution system; but the results of transformer loading rates calculated by this model are usually unbalanced, which is unacceptable in the practical operation. In order to solve this problem, this paper discusses the solution properties of the TSC model first. It is found that the transformer loading rates vector is not unique under the same TSC level; and only one of the optimal solution can be obtained from the linear programming software such as lingo. Then, an improved TSC model is proposed, which takes variance of loading rates and heavy-load areas into account. This new model is divided into two parts, the first part is the TSC calculation process, and the second part is the most balanced loading rate calculation process. Finally, a practical case is given to illustrate the effect of the improved model. It has shown that the loading rates calculated through this method are more balanced than the original one under the same TSC. The method can be useful tools to help system planners to get the best configuration of transformers in distribution systems.
A Short-Term and High-Resolution Distribution System Load Forecasting Approach Using Support Vector Regression With Hybrid Parameters Optimization
This paper proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of the hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system. The performance of the proposed approach is compared to some classic methods in later sections of this paper.
Career Choice Inclination of the College Graduates——under the Analytic Framework on the Basis of LMS Theory
Employment difficulty of college graduates has become a social problem in China today.The research group has found that college graduates have a strong inclination in career choice considering units and regions.In terms of China's manpower market and the segmented labor market theory,it is found that their inclination is caused by the great income gap of different regions,the high cost of job transfer,and the sharp differences in social welfare.It is thus obvious that solution to the problem lies with the gradual breakdown of the institutional segment of labor market.