Yanchi Zhang

A New Solution for Stochastic Optimal Power Flow: Combining Limit Relaxation with Iterative Learning Control

A stochastic optimal power flow (S-OPF) model considering uncertainties of load and wind power is developed based on chance constrained programming (CCP). The difficulties in solving the model are the nonlinearity and probabilistic constraints. In this paper, a limit relaxation approach and an iterative learning control (ILC) method are implemented to solve the S-OPF model indirectly. The limit relaxation approach narrows the solution space by introducing regulatory factors, according to the relationship between the constraint equations and the optimization variables. The regulatory factors are designed by ILC method to ensure the optimality of final solution under a predefined confidence level. The optimization algorithm for S-OPF is completed based on the combination of limit relaxation and ILC and tested on the IEEE 14-bus system.

A new-style dynamic var compensation control strategy

A new dynamic reactive power compensation technique for dynamic var compensator is presented. This paper presents the DC capacitor voltage control strategy, the loss current variation control algorithm and a new reactive current calculating method. And the desired compensating current is compared with the measurement of output current in the hysteretic current feedback-loop. Based on the analysis, a new- style dynamic var compensation control strategy is realized. It also analyzes the relationship between DC capacitor voltage variation characteristic and the active and reactive power flow between AC and DC sides. A novel dynamic var compensation prototype is developed. The experimental waveforms of the prototype show that this control algorithm has the expected compensating performance, demonstrating the validity of the design scheme proposed in this article.