This article discusses a new methodology, which combines two efficient methods known as Monte Carlo (MC) and Stochastic‐algebraic (SA) methods for stochastic analyses and probabilistic assessments in electric power systems. The main idea is to use the advantages of each former method to cover the blind spots of the other. This new method is more efficient and more accurate than SA method and also faster than MC method while is less dependent of the sampling process. In this article, the proposed method and two other ones are used to obtain the probability density function of different variables in a power system. Different examples are studied to show the effectiveness of the hybrid method. The results of the proposed method are compared to the ones obtained using the MC and SA methods. © 2014 Wiley Periodicals, Inc. Complexity 21: 100–110, 2015

Short-Term Price Forecast is a key issue for operation of both regulated power systems and electricity markets. Energy price forecast is the key information for generating companies to prepare their bids in the electricity markets. However, this forecasting problem is complex due to nonlinear, nonstationary, and time variant behavior of electricity price time series. So, in this article, the forecast model includes wavelet transform, autoregressive integrated moving average, and radial basis function neural networks (RBFN) is presented. Also, an intelligent algorithm is applied to optimize the RBFN structure, which adapts it to the specified training set, reduce computational complexity and avoids over fitting. Effectiveness of the proposed method is applied for price forecasting of electricity market of mainland Spain and its results are compared with the results of several other price forecast methods. These comparisons confirm the validity of the developed approach.

This article focus on optimal economic load dispatch based on an intelligent method of shark smell optimization (SSO). In this problem, the risk constrains has been considered which has root in uncertainity and unpredictable behavior of wind power. Regarding to increasing of this clean energy in power systems and un‐dispatchable behavior of wind power, its conditional value at risk index considered in this article which consists of loss from load and "spilling" wind energy connected with unpredictable imbalances among generation and load. This problem has been considered as an optimization problem based on SSO that evaluate the balance between cost and risk. This algorithm is based on distinct shark smell abilities for localizing the prey. In sharks' movement, the concentration of the odor is an important factor to guide the shark to the prey. In other words, the shark moves in the way with higher odor concentration. This characteristic is used in the proposed SSO algorithm to find the solution of an optimization problem. Effectiveness of the proposed method has been applied over 30‐bus power system in comparison with other techniques. © 2016 Wiley Periodicals, Inc. Complexity 21: 494–506, 2016