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Abstract-This paper presents the application of an efficient shuffled frog leaping algorithm (SFLA) to solve the optimal generation expansion planning (GEP) problem. The SFLA is a meta-heuristic search method inspired by natural memetics. It combines the advantages of both genetic-based memetic algorithms and social behavior based algorithm of particle swarm optimization. Least-cost GEP is concerned with a highly constrained non-linear discrete dynamic optimization problem. In this paper the proposed formulation of problem, determines the optimal investment plan for adding power plants over a planning horizon to meet the demand criteria, fuel mix ratio, and the reliability criteria. To test the proposed SFLA method, it is simulated for two test systems in a time horizon of 10 and 20 years respectively. The obtained results show that compared to the traditional methods, the SFLA method can provide better solutions for the GEP problem, especially for a longer time horizon.Index Terms-Generation expansion planning, probabilistic production simulation, shuffled frog leaping algorithm. I. INTRODUCTIONThe Generation Expansion Planning (GEP) is a problem to determine when, where, what type and how much capacity of new power plants should be constructed over a long-term planning horizon to meet forecasted demand according to a pre-specified reliability criteria. GEP is an important decision-making activity for utility companies. The main objective of GEP is to minimize the total investment, operating, and outage (energy-not-served) costs of power system.There are generally two deterministic and stochastic approaches to solve the GEP problem. The Stochastic approach takes into account uncertainties associated with the input data, such as forecasted demand, fuel prices, economic and technical characteristics of new evolving generating technologies, construction lead times, and governmental regulations [1]. The deterministic approach solves the problem under different scenarios. In this case a fast and efficient method is required because of great number of simulations needed to be run under different scenarios.Least-cost GEP is concerned with a highly constrained non-linear discrete dynamic optimization problem. The high non-linearity of a GEP problem originates from the nature of the production cost and the set of non-linear constraints. The dynamic programming (DP) approach is one of the most used Manuscript received February 20, 2012; revised March 16, 2012 algorithms in GEP. However in the generation expansion problem, due to high dimensionality, the DP is not an efficient method for real power systems. In commercial packages like WASP [2], to overcome this difficulty, heuristic tunnel-based techniques are used in DP routine, where users pre-specified configurations and successively modified tunnels are considered to arrive at local optimums.The emerging optimization techniques used to solve GEP problem were reviewed in [3]. In [4] the meta-heuristic techniques; such as Genetic Algorithm, Differential Evolution, Evolu...

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.