Boming Zhang

Wenchuan Wu, Zhuang Tian, Boming Zhang

The branch flow based optimal power flow(OPF) problem in radianlly operated distribution networks can be exactly relazed to a second order cone programming (SOCP) model without considering transformers. However, the introdution of nonlinear transformer models will make the OPF model non-convex. This paper presents an exact linearized transformer's OLTC model to keep the OPF model convex via binary expanstion scheme and big-M method. Validity of the proposed method is verified using IEEE 33-bus test system.

Zihao Li, Wenchuan Wu, Boming Zhang

For its high coefficient of performance and zero local emissions, the heat pump (HP) has recently become popular in North Europe and China. However, the integration of HPs may aggravate the daily peak-valley gap in distribution networks significantly.

Xin Chen, Wenchuan Wu, Boming Zhang

To settle a large-scale integration of renewable distributed generations (DGs), it requires to assess the maximal DG hosting capacity of active distribution networks (ADNs). For fully exploiting the ability of ADNs to accommodate DG, this paper proposes a robust comprehensive DG capacity assessment method considering three-phase power flow modelling and active network management (ANM) techniques. The two-stage adjustable robust optimization is employed to tackle the uncertainties of load demands and DG outputs. With our method, system planners can obtain the maximum penetration level of DGs with their optimal sizing and sitting decisions. Meanwhile, the robust optimal ANM schemes can be generated for each operation time period, including network reconfiguration, on-load-tap-changers regulation, and reactive power compensation. In addition, a three-step optimization algorithm is proposed to enhance the accuracy of DG capacity assessment results. The optimality and robustness of our method are validated via numerical tests on an unbalanced IEEE 33-bus distribution system.

Weiye Zheng, Wenchuan Wu, Boming Zhang et al.

In distribution networks, there are slow controlling devices and fast controlling devices for Volt-VAR regulation. These slow controlling devices, such as capacitors or voltage regulators, cannot be operated frequently and should be scheduled tens of minutes ahead (Hereafter named as slow control). Since of the uncertainties in predicting the load and distributed generation, the voltage violations cannot be eliminated by fast controlling devices with improper schedule of the slow controlling devices. In this paper we propose dual time-scale coordination for the Volt-VAR control scheme, corresponding to slow and fast control. In the case of slow control, a robust voltage and reactive power optimization model is developed. This guarantees that subsequent fast controls can maintain the system's voltage security if the uncertain parameters vary within predefined limits. This nonconvex optimization problem is relaxed to a mix integer second order conic problem, and the dual form of its sub-problem is also derived. Then a column-and-constraint generation algorithm was used to solve the robust convexified model. A conventional deterministic optimization model can be used to determine the fast control mechanism. Numerical tests were conducted on a real distribution feeder in China, a balanced IEEE 69-bus and unbalanced 123-bus benchmark distribution networks. The simulation results show that solving the deterministic model is not always feasible and voltage violation may occur. The robust model was shown to be effective with respect to all possible scenarios in the uncertainty set, with little compromise in terms of network losses.

Weiye Zheng, Wenchuan Wu, Antonio Gomez-Exposito et al.

This paper proposes a fully distributed robust state-estimation (D-RBSE) method that is applicable to multi-area power systems with nonlinear measurements. We extend the recently introduced bilinear formulation of state estimation problems to a robust model. A distributed bilinear state-estimation procedure is developed. In both linear stages, the state estimation problem in each area is solved locally, with minimal data exchange with its neighbors. The intermediate nonlinear transformation can be performed by all areas in parallel without any need of inter-regional communication. This algorithm does not require a central coordinator and can compress bad measurements by introducing a robust state estimation model. Numerical tests on IEEE 14-bus and 118-bus benchmark systems demonstrate the validity of the method.