For large-scale distribution networks (DNs) with distributed generations (DGs), the conventional centralized Volt/Var optimization and control (VOC) have high demand for online computing resources and efficiency. This paper proposes a new decision-making method for the distributed VOC (D-VOC) based on a two-layer collaborative architecture. First, for a distributed equivalent load system with on-load tap changer (OLTC), this paper studies how to minimize the active power loss and reach the target voltage of load side for the equivalent load system. Second, a concept of virtual soft controller (VSC) is presented based on the distributed equivalent load system, and the local optimal control strategies of VSCs are designed to realize the D-VOC in DNs. On this basis, a two-layer collaborative architecture for the D-VOC is designed. VSCs could be built in virtual or real deployment at nodes where reactive power sources or reactive power compensation devices are equipped, and they are at the bottom layer of the architecture. VSCs upload their distributed decision results to the local control center (LCC) which is at the top layer of the architecture. The LCC systematically verifies and authorizes each VSC to execute its own operational instruction. Case study on the modified PG & E 69-bus distribution grid shows that the method is feasible and has satisfactory efficiency. INDEX TERMS Distribution network, distributed optimization and control, reactive power, voltage, twolayer architecture, virtual soft controller.

The forecast for photovoltaic (PV) power generation is of great significance for the operation and control of power system. In this paper, a short-term combination forecasting model for PV power based on similar day and cross entropy theory is proposed. The main influencing factors of PV power are analyzed. From the perspective of entropy theory, considering distance entropy and grey relation entropy, a comprehensive index is proposed to select similar days. Then, the least square support vector machine (LSSVM), autoregressive and moving average (ARMA), and back propagation (BP) neural network are used to forecast PV power, respectively. The weights of three single forecasting methods are dynamically set by the cross entropy algorithm and the short-term combination forecasting model for PV power is established. The results show that this method can effectively improve the prediction accuracy of PV power and is of great significance to real-time economical dispatch.

When the large-scale wind power is sent out through the high voltage direct current (HVDC) transmission system and a DC commutation failure occurs, the voltage of AC bus at the sending end decreases first and then increases. Suppose the reactive power supported in the low voltage ride-through process by various reactive resources is not timely returned. In that case, it may aggravate the voltage rise caused by the commutation failure, and the off-grid risk of wind turbine under high-voltage will be aggravated. In order to reduce the off-grid risk of wind turbines caused by the DC commutation failure, a transient voltage control strategy of DC sending-end regulator based on the online sequential extreme learning machine (OS-ELM) voltage prediction model is proposed. Firstly, the influence factors of commutation failures are analyzed. Aiming at the key factors, the real-time voltage comprehensive prediction model based on OS-ELM is used to predict the voltage increase during the commutation failure process and uses the voltage prediction results to optimize the transient response of the synchronous condenser. A large-scale wind farm together with the HVDC system is established in PSCAD to verify the effectiveness of the proposed scheme. Simulation results show that the proposed scheme can reduce the risk of wind power off-grid risk under DC commutation failures and increase the speed of voltage recovery at the point of common coupling. INDEX TERMSDC commutation failure; high voltage ride through; doubly-fed induction generator; synchronous condenser; transient voltage control

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