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SUMMARYRecent studies have shown that power system blackouts occur much more likely than might be expected, and their probability distribution follows power law. Cascading outages have been reported as the major cause of the large blackouts; therefore, the risk of cascading outages is significant and should be considered in operating and planning assessments of the power systems. The Oak Ridge National Laboratory, Power Systems Engineering Research Center and University of Alaska (OPA) is a model developed on the basis of self-organized criticality to study cascading outages of transmission lines. In this paper, a modified version of the OPA model is used for considering risk of cascading transmission line outages in transmission expansion planning. The proposed method finds a set of effective candidate lines having higher capability for suppressing cascading outages; then, the original OPA is exploited over the planning horizon to analyze long-term reliability of the system. The benefit of each prospective transmission line is derived by the following two innovations in calculating the risk of blackouts: (i) the power law for finding the probability of blackouts; and (ii) a nonlinear estimation of the cost of the blackouts. Accordingly, the savings associated to each candidate line is calculated. These values are used to find the optimal plan using benefit/cost analysis. Two IEEE test systems are investigated to examine the applicability and scalability of the proposed method. The investigations revealed that the proposed method provides more effective scenarios, which entail considerable saving to society.

Summary Demand response (DR) programs are effective tools to relieve power systems operational risks and will play key role in future smart grids because of emerging advanced communication and metering technologies. Enhancing power system reliability is one of the main goals of employing DR programs. This paper presents a nodal viewpoint for reliability‐based planning of DR programs. It aims to demonstrate the superiority of nodal evaluation and prioritization of DR programs to improve reliability over the global approach. Here, a sequential state‐enumeration‐based method is presented for composite system operational reliability assessment, and a multi‐segment optimal power flow approach is developed to evaluate the effects of DR programs. In addition, analytic hierarchy process is employed for global and nodal prioritizing of different DR programs. The results show that employing DR programs based on nodal prioritization can lead to higher reliability, lower costs, and greater assurance on programs implementation. Copyright © 2014 John Wiley & Sons, Ltd.

Recent developments in traditional power systems which involve emerging smart technologies and widely employing of communication will convert the present electricity grids into the smart grids. The future smart and efficient power systems will treat completely different compare with the existing power systems. This paper discusses the effect of emerging smart grids on the consumer's behavior. It investigates the responses of different types of consumers to the spot electricity price and the price elasticity of demand in the smart grid environment. Smart technologies could bring all of the consumers with any level of demand to the market actively, and results in increasing the efficiency of the market in a fully competitive electricity market. This paper also describes the effect of Demand Response (DR) on some electricity market issues like short-term load and price forecasting, generation expansion, and imperfect competition, in the smart grid environment. The qualitative discussions show that by emerging the smart grids the market efficiency, costumer's benefits, and Demand Response of the power system are improved and the ability of strategic players to exert market power will be reduced. Index Terms-Advanced Metering Infrastructure (AMI), Demand Response (