This paper proposes an analytical method that incorporates the time of use (TOU) strategy into the reliability evaluation of power system. The TOU electricity price strategy enables the savings of electricity cost, but its impact on power system reliability has not been thoroughly studied. The proposed method constructs a peak-flat-valley period partition model and develops an equal step length iteration algorithm to obtain the optimal period partition. A price-based demand response is modeled to calculate the hourly load using an apportionment method. Using the particle swarm optimization algorithm, a TOU-based optimization model with a penalty is constructed to find the optimal electricity price. Then, an analytical “electricity price-reliability” model is developed to evaluate the reliability of the power system considering TOU. The effectiveness of the proposed method is investigated through the case study on the Roy Billinton test system and reliability test system.

Energy production of a wind farm is greatly affected by the wake-effect. In wind farm planning, it is essential to optimize the layout of wind turbine generators (WTGs) to alleviate the impact of wake-effect. This paper presents a new wind farm layout optimization model with an objective function to maximizing equivalent power of the wind farm. The proposed model incorporates the discrete joint probability distribution and characteristics of the wind, wind energy model, and the wake-effect. A particle swarm optimization algorithm is used to solve the optimization problem. In the optimization process, geometric analysis is conducted to determine the upstream WTGs and to calculate the horizontal and deviating distances among WTGs along the wind direction. An effective look-up table is constructed to directly determine the upstream/downstream WTGs. Then the optimal layout of a wind farm can be obtained. The proposed technique is applied to the wind farm layout optimization and the modified Roy Billinton Test System and IEEE Reliability Test System for reliability analysis of a generating system containing a wind farm. Simulation results demonstrate the validity and flexibility of the proposed technique in the layout optimization of WTGs in a wind farm.

An accurate probability distribution model of wind speed is critical to the assessment of reliability contribution of wind energy to power systems. Most of current models are built using the parametric density estimation (PDE) methods, which usually assume that the wind speed are subordinate to a certain known distribution (e.g. Weibull distribution and Normal distribution) and estimate the parameters of models with the historical data. This paper presents a kernel density estimation (KDE) method which is a nonparametric way to estimate the probability density function (PDF) of wind speed. The method is a kind of data-driven approach without making any assumption on the form of the underlying wind speed distribution, and capable of uncovering the statistical information hidden in the historical data. The proposed method is compared with three parametric models using wind data from six sites. The results indicate that the KDE outperforms the PDE in terms of accuracy and flexibility in describing the long-term wind speed distributions for all sites. A sensitivity analysis with respect to kernel functions is presented and Gauss kernel function is proved to be the best one. Case studies on a standard IEEE reliability test system (IEEE-RTS) have verified the applicability and effectiveness of the proposed model in evaluating the reliability performance of wind farms.

In recent years, the performance of polluted suspension insulators operating in coastal foggy regions has attracted attention because of the occurrence of flashover. Surface wetting by salt fog accretion increases the surface conductivity and degrades the flashover performance of insulators. Normally, the effect of conductivity under fog condition and pre-contamination on the flashover of polluted insulators are analyzed separately. However, this does not reveal the flashover behavior in salt fog, which requires further research. This paper describes salt fog experiments with an AC test voltage conducted using polluted suspension insulators in a 110-kV power system. The electrical strength performance of sample insulators with salt fog treatments is analyzed, and the effects of salt deposit density (SDD) and the conductivity of fog water (CFW) on the flashover of three types of suspension insulators are studied. The concept of additional SDD is proposed to quantify analyze the effects of salt fog. The results show that there is less dependence on CFW at higher pollution levels than at lower pollution levels. That is, the effect of CFW on U50 can be neglected for SDD>0.15 mg/cm2, whereas CFW has a strong effect on U50 for SDD<0.15 mg/cm2. CFW has a greater influence on the critical flashover voltage of ceramic insulators than on that of silicone rubber insulator. A model for calculating the critical flashover voltage of insulators is proposed by considering the combined effect of CFW and pre-polluting SDD.

Time-of-use (TOU) is an effective price-based demand response strategy. A reasonable design of TOU strategy can effectively reduce the peak-valley difference, and then produce a lot of benefits (such as delaying power grid investment, reducing interruption cost, and improving reliability). However, changing peak-valley period has a great influence on the peak-valley difference and power supply reliability of power system. Therefore, this paper aims to investigate the optimal modification of peak-valley period considering reliability loss under multiple TOU schemes. Firstly, this paper presents a clustering model and algorithm of optimal load curve based on a minimum error iteration method. Secondly, an optimal modification of peak-valley period based on a dynamic load point method is proposed, and the traditional peak-valley difference is replaced by the global peak-valley difference to calculate the objective function. Thirdly, this paper establishes a load–reliability relation fitting model based on the back propagation neural network. Finally, the effectiveness and correctness of the proposed method are investigated by the Roy Billinton test system and reliability test system.