Wind power has been considered a future alternative to fossil energy resources. However, due to its stochastic nature, the integration of wind power plants (WPPs) into power systems poses some reliability problems such as a mismatch between Load profile and efficient wind power generation. This issue can be alleviated by considering the correlation between hourly Load and wind speed variations in the planning phase. To this end, a reliability-based wind power planning procedure is proposed and formulated as a stochastic programming problem. The objective function is the minimization of total costs, including capital investment, operating and maintenance, and customer energy not served costs. A new hybrid method that combines features of the Load-duration curve and the K-means clustering algorithm is proposed to Model the uncertainty of the input data. A shuffled frog-leaping algorithm is used to solve the proposed Model. The simulation results indicate that the amount of adaptation between hours with high Loads and those with high wind speeds markedly affects the selection of wind sites as optimal locations for WPP installation. Considering this issue can also improve power system reliability in the presence of WPPs.

This paper presents the results from 92 fire Load density surveys conducted in 52 office buildings of Pakistan. The combination method of surveying that includes both inventory and weighing methods is used to determine the fire Load of 92 office rooms, including 44 private and 48 government offices. Multiple linear regression analysis techniques are applied to assess the relationship of Fire Load Density (FLD) with variables according to the characteristics of the office rooms, such as office type, category, combustible materials, room dimensions, and ventilation conditions. Probabilistic Models for FLD are developed using the regression analysis of the survey data. The survey data is further used to determine the maximum fire intensity in office buildings in Pakistan. The survey results show that the FLD increases with the increase in the area of the office. The percentage of wood is found to be the most contributing factor in the fire Load. It has been noted that the fire Load values are different for government and private offices, whereas the Building Code of Pakistan (BCP) has the same value for both. Statistical results presented in this study will be helpful in the fire safety and fire-resistant structure design of buildings in Pakistan.

Considering economic and environmental factors, it is expected that the number of plug-in electric vehicles (PEVs) will be increased, rapidly. The high penetration of EVs, can affect the power system. Therefore, in recent years, various studies have paid their attention to the impacts of PEVs charging on the network. In this paper, a Probabilistic Model based on the queueing theory is extracted using Monte Carlo simulation for Modeling EV charging station Load. It is assumed that the vehicles are the taxis of Amol city in Mazandaran province. Required data such as the time of arrival and the state of charge of the battery before charging, were collected and extracted using three methods from intra-city taxis in the city of Amol. To obtain the demand Load of EV charging, the traffic-based behavior of drivers is needed. This behavior is stochastic. Therefore, its related variables will not be deterministic and must be evaluated using Probabilistic methods.

In this paper a distributed algorithm for Load balancing in cellular mobile systems, which is based on Probabilistic cellular automata, is presented. The proposed algorithm unlike existing algorithms provides higher degree of independence for each cell of cellular systems for decision-making. Simulations results show that the proposed algorithm performs well under medium Load.

Optimal Reactive Power Dispatch (ORPD) is a multi-variable problem with nonlinear constraints and continuous/discrete decision variables. Due to the stochastic behavior of Loads, the ORPD requires a Probabilistic mathematical Model. In this paper, Monte Carlo Simulation (MCS) is used for Modeling of Load uncertainties in the ORPD problem. The problem is formulated as a nonlinear constrained multi objective (MO) optimization problem considering two objectives, i.e., minimization of active power losses and voltage deviations from the corresponding desired values, subject to full AC Load flow constraints and operational limits. The control variables utilized in the proposed MO-ORPD problem are generator bus voltages, transformers’ tap ratios and shunt reactive power compensation at the weak buses. The proposed Probabilistic MO-ORPD problem is implemented on the IEEE 30-bus and IEEE 118-bus tests systems. The obtained numerical results substantiate the effectiveness and applicability of the proposed Probabilistic MO-ORPD problem.

Nowadays, smart grid implementation is a new and challenging issue. One of the important parameters in the smart grids is their fast and accurate fault locator capability. This capability reduces power outage time, which the repair teams of the power distribution companies mostly lose to find the fault location in a traditional way. The fault allocation in the distribution networks, due to their topological nature (high complexity, intermediate Loads, Load uncertainty, no homogeneity of lines, etc. ), is a very difficult and different from transmission networks. In this research, fault indicator information is used to deal with multiple result problem introduced by fault locator in distribution networks. In this paper, a forward-backward sweep based method is introduced to find fault location. There are some uncertain parameter in fault location because of the error on the Load estimation. There is correlation between these parameters due to similarity of the environmental conditions and customer behavior. Therefore, two methods (Monte Carlo and two-point estimation) are used to Model Probabilistic parameters on fault location. Finally, the 11-bus test network and the actual 306-bus Sharfabad feeder network in Kerman are utilized to evaluate the proposed method.

With the growth and integration of distributed generation resources in smart grids, net Load forecasting is of significant importance. A hybrid optimization method is proposed in this paper for Probabilistic net Load forecasting using neighborhood component analysis and solving regression problem with the aid of mini-batch LBFGS method. Net Load forecasting is suggested in this paper trough forecast combination via adaptive network-based fuzzy inference system. The structure includes a combination of several long-term forecasts, including forecasts of Load, the generation of a solar station, and the generation of a wind farm with wind turbines equipped with doubly-fed induction generator. Also, the net Load forecasting and the relationship between errors of Load, wind and solar predictions are studied in this paper. The simulation results of the proposed method and its comparison with Tao and quantile regression Models show that mean absolute percentage error of Load forecasting, and the forecasts of solar and wind generations improved by 0. 947%, 0. 3079% and 0042%, respectively which result to a decrease in net Load forecasting error.

The increase in power generated by using solar energy increases the uncertainty in the power grid, since the majority of meters measure only the net Load of a grid regardless of the output of distributed generations. This paper proposes a framework for Probabilistic mid-term net Load forecasting in a power grid based on separate forecasts of the Load and output power of a solar station using the combination of principal components analysis and the extreme learning machine methods. The data used in this paper is related to the NERL and GEFCom2014 data bases and the matrix of scores is extracted by the use of principal component analysis. The prediction Models are trained using ORELM Model and evaluated in three sections: training, validation and midterm prediction. The main objective of the proposed method is to increase the precision of net Load forecasting by improving point forecasts. The comparison between the results presented in this thesis with other references shows that the MAPE error of predicted Load, and predicted output power of the solar station improved to 1. 1333 and 0. 3118, respectively, which will reduce overall forecast error.

Inventory control systems are supposed that the purchasing cost is paid at the time of delivery of product. But sometimes in order to motivate the customers to buy more, supplier lets customers to pay the purchasing cost under delay payment method as an incentive policy. In this paper a periodic inventory control Model is considered in which the interval between two replenishments is a random variable. Indeed the economic order quantity Model is developed under two policies including delayed payment and stochastic replenishment interval. It is proved that the profit function of retailer is concave and the main aim of this paper is to determine the maximum level of inventory such that the profit of retailer is maximized. In continue in order to show the applicability of the proposed Model, several numerical examples and sensitivity analyses have been presented.