Research On Stochastic Estimation And Economic Management Of Voltage Sag

The voltage sags have become the major cause that disturbs the normal operation of equipments and leads to huge financial loss of consumers with the widespread use of new electronic devices in power system. Voltage sag assessment and management are becoming important for safety of power system operation, power quality analysis and demand side management, but presently principle voltage sag analysis and assessment methods can not meet the requirements of power engineering. In this paper, the stochastic estimation method and economic management method of voltage sags are researched systematically. From the system fault model, load model and probabilistic simulation of uncertainty, improved assessment methods of voltage sags are proposed to guide the voltage sag investment and management. The main contributions of this paper are as follows:(1) The system reconfiguration method (RCS) is presented for stochastic estimation of voltage sags in complex distribution systems. From the reconfiguration of compound sequence network of system failures, the RCS models for three-phase fault, single line-to-ground fault, line-to-line fault and double line-to-ground fault are established. On the basis of ITIC curve of sensitive loads, the area of vulnerability and assessment indices of voltage sag for Point of Common Coupling (PCC) under faults are obtained, respectively. The impact of system fault clearing time and transformer connection style on voltage sag indices of PCC is analyzed and discussed.(2) The stochastic estimation model of voltage sags is established considering load characteristics, and Newton-style iterative format with polynomial load model is used. Because the power flow equations under faults are ill-conditioned, the area of vulnerability and PCC indices of voltage sag are calculated by adaptive trust region method and Levenberg-Marquardt method (LM method) with variable step-size, and indicating the impact of current and power load on assessment indices. The bifurcation point is proposed to analyze the convergences of optimal multiplier method, adaptive trust region method and LM method with variable step-size. The convergences of iterative format from direct method and Newton-style format are compared.(3) The Monte-Carlo simulation is used for probabilistic assessment under uncertainty. Uncertain factors such as stochastic start-stop of distributed generation, load fluctuation, fault duration and financial loss property of load points are considered by the assessment method, on which the density indices of load point and ESF indices of sensitive loads are calculated. The financial loss due to voltage sags are estimated in probability method based on the specific energy function of sensitive equipments considering the classification of sensitive equipments.(4) The optimal investment and allocation strategy of voltage sag are presented with sensitivity indices to voltage sag. The Nash equilibrium theory is adopted for calculating total investment and optimal sag reduction; the optimal allocation of sag investment is completed with interactive TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method, and the treatment technology difference of four types of voltage sag is considered during the decision process. Interactive method is used to determine the weight vector and get the superior information of load points for objectivity and rationality.