This paper proposes a stochastic wind power model based on an autoregressive integrated moving average (ARIMA) process. The model takes into account the nonstationarity and physical limits of stochastic wind power generation. The model is constructed based on wind power measurement of one year from the Nysted offshore wind farm in Denmark. The proposed limited-ARIMA (LARIMA) model introduces a limiter and characterizes the stochastic wind power generation by mean level, temporal correlation and driving noise. The model is validated against the measurement in terms of temporal correlation and probability distribution. The LARIMA model outperforms a first-order transition matrix based discrete Markov model in terms of temporal correlation, probability distribution and model parameter number. The proposed LARIMA model is further extended to include the monthly variation of the stochastic wind power generation.

In this paper, a modified auto regressive integrated moving average (ARIMA) modeling procedure that can capture time correlation and probability distribution of observed wind-speed time-series data is presented. The procedure introduces frequency decomposition (splitting the wind-speed data into high frequency (HF) and low-frequency (LF) components), shifting, and limiting in addition to differencing and power transformation which are used in the standard ARIMA modeling procedure. The modified modeling procedure is applied to model 10 minute average measured wind-speed data from three locations in the Baltic Sea area and the results show that the procedure can capture time correlation and probability distribution of the data. In addition, it is shown that, for 10-min average wind-speed data in the Baltic Sea area, it could be sufficient to use ARIMA(6,0,0) and ARIMA(0,1,6) to model the HF and LF components of the data, respectively. It is also shown that, in the Baltic Sea area, a model developed for an observed wind-speed data at one location could be used to simulate wind-speed data at a nearby location where only the average wind-speed is known.

This paper reviews the development of the probabilistic load flow (PLF) techniques. Applications of the PLF techniques in different areas of power system steady-state analysis are also discussed. The purpose of the review is to identify different available PLF techniques and their corresponding suitable applications so that a relatively accurate and efficient PLF algorithm can be determined for the concerned system, e.g. a distribution system with large integration of renewable energy based dispersed generations.

The increased spreading of distributed and renewable generation requires moving towards active management of distribution networks. In order to evaluate maximum wind energy exploitation in active distribution networks, a method based on a multi-period optimal power flow analysis is proposed. Active network management schemes such as coordinated voltage control, energy curtailment and power factor control are integrated in the method in order to investigate their impacts on the maximisation of wind energy exploitation. Some case studies, using real data from a Danish distribution system, confirmed the effectiveness of the proposed method in evaluating the optimal applications of active management schemes to increase wind energy harvesting without costly network reinforcement for the connection of wind generation.

Active management strategies such as coordinated on load tap changer (OLTC) voltage control and reactive power compensation (RPC) are frequently suggested for voltage regulation in a distribution system with a high level of distributed generation (DG). This paper proposes a control and coordination algorithm for these two active management strategies. Voltage control through OLTC is achieved by using state estimation (SE) to determine the voltage in the network. To lower the implementation cost of the proposed control strategy, pseudo-measurements are used together with real-time measurement data in the SE. Moreover, the deadband of the automatic voltage control (AVC) relay is relaxed so that the AVC relay acts on the network's maximum or minimum voltage obtained through the SE. This is found to be simpler to realize than adjusting the set point of the AVC relay. Voltage control through RPC is actualized by using integral controllers implemented locally at the wind turbine site. Furthermore, RPC from the local wind turbine is also used to mitigate an overvoltage at a remote bus on the same feeder when the remote wind turbine reaches its regulation limit. The applicability of the proposed voltage regulation algorithm is successfully demonstrated using a case study system.

This paper proposes a stochastic optimization algorithm that aims to minimize the expectation of the system power losses by controlling wind turbine (WT) power factors. This objective of the optimization is subject to the probability constraints of bus voltage and line current requirements. The optimization algorithm utilizes the stochastic models of wind power generation (WPG) and load demand to take into account their stochastic variation. The stochastic model of WPG is developed on the basis of a limited autoregressive integrated moving average (LARIMA) model by introducing a cross-correlation structure to the LARIMA model. The proposed stochastic optimization is carried out on a 69-bus distribution system. Simulation results confirm that, under various combinations of WPG and load demand, the system power losses are considerably reduced with the optimal setting of WT power factor as compared to the case with unity power factor. Furthermore, an economic evaluation is carried out to quantify the value of power loss reduction. It is demonstrated that not only network operators but also WT owners can benefit from the optimal power factor setting, as WT owners can pay a much lower energy transfer fee to the network operators.