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Photovoltaic power generation is gradually developing into a massive power industry with the maturity of renewable energy power generation technologies. Photovoltaic power generation is greatly affected by external factors and the output power is characterized by randomness and indirectness, which poses a great challenge to photovoltaic grid-connection. A hybrid improved multi-verse optimizer algorithm (HIMVO) is proposed to optimize the support vector machine for photovoltaic output prediction. HIMVO algorithm introduces chaotic sequences to initialize the population, which significantly enhances the convergence rate of the algorithm compared with the multi-universe optimizer algorithm. This study applied particle swarm optimization algorithm, dragonfly algorithm, multi-universe optimizer algorithm and HIMVO to testify the availability of the hybrid improved multi-verse optimizer support vector machine model (HIMVO-SVM). The results indicate that HIMVO algorithm has better optimization ability and stability. The four models, HIMVO-SVM, multi-verse optimizer support vector machine, particle swarm optimization support vector machine, back propagation and radical basis function neural network are used to predict output in three different weather types. The results indicate that the model has higher prediction accuracy and stability. The mean square error value of the HIMVO-SVM model decreases by at least 0.0026, 0.0030 and 0.0012, and the mean absolute percentage error value decreases by at least 3.6768%, 1.9772% and 2.7165%, respectively. The proposed method is beneficial to the prediction of output power and conduces to the economic dispatch of the grid and the maintenance of the stability of the power system.

Voltage stability of power grid would be affected greatly by the connection of large-scale wind farms. Conventional continuous power flow method can not accurately simulate the system’s operation when wind farms are connected to power gird. A new continuous power flow method is proposed, considering auto-disconnection of large-scale wind farms. The step-size control segment is improved in this algorithm in order to simulate the operation characteristics of wind farm and determine the precise time when a certain wind farm is cut off.

DC/DC converters are known to exhibit bifurcations and chaos if switching actions are governed by feedback control. A dynamic controller based optimal control method is presented to control the chaos behavior and maximize the conversion efficiency of a zero-voltage-switching (ZVS) buck converter operating in continuous conduction mode (CCM). The simulation results show the effectiveness of the proposed scheme.

Given the current operating condition (obtained from the real-time data), the near-term load demand at each bus (obtained from short-term load forecast), and the generation dispatch (say, based on economic dispatch), we present in this paper a load margin measure (MW and/or MVAr) to assess the system's ability to withstand the forecasted load and generation variations. We also present a method to predict near-term system voltage profiles. The proposed look-ahead measure and the proposed voltage prediction are then applied to contingency selections for the near-term power system in terms of load margins to collapse and of the bus voltage magnitudes. We evaluate the proposed look-ahead measure and the voltage profile prediction on several power systems including a 1169-bus power system with 53 contingencies with promising results.

Because of large fluctuations and strong randomness of active power generated by renewable energy resources, taking into account the constraints such as battery life cycle, a new battery energy storage system control method based on real-time state-of-charge and variable filter time constant is presented. This method could compensate the fluctuation composition of a particular frequency band of power. At the same time, filter time constant is adjusted in order to stabilize the value of SOC in a certain range, and over-charging and over-discharging of the battery is avoided. A photovoltaic (PV) and battery hybrid system modeled by PSCAD is tested to verify the validity of this method.

A very fast method for computing the saddle-node bifurcation point of general nonlinear systems with decoupled parameters is developed in this paper. The speed of the developed method is achieved through two factors. One factor is a new set of characteristic equations for the saddle-node bifurcation point which is of dimension n+1, instead of 2n+1, which is traditionally required in n-dimensional decoupled parameter-dependent nonlinear equations. The other factor is a new effective scheme for estimating the location of a desired saddle-node bifurcation point and its associated bifurcation value. The estimated location and value provide a good initial guess for computing the desired saddle-node bifurcation points. The proposed solution method has been applied to a set of 416-dimensional decoupled parameter-dependent nonlinear equations to illustrate its effectiveness and accuracy.

The objective of this paper is to present a powerful procedure for identifying severe single branch outage contingencies with respect to saddle-node bifurcation, given a power system operating point, a load demand forecast and a generation dispatch. A novel two-stage look-ahead ranking algorithm for saddle-node bifurcation are described. The first stage is based on a branch MVAr participation index and the second stage is based on sensitivity analysis. The distinguishing features are the ability: (i) to screen out the weak contingencies; and (ii) to directly estimate the strong contingency bifurcation values. The novel two-stage look-ahead ranking algorithm has been applied to a 3493 bus power system with promising results.