• Title, Summary, Keyword: Stochastic optimal power flow

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A New Solution for Stochastic Optimal Power Flow: Combining Limit Relaxation with Iterative Learning Control

  • Gong, Jinxia;Xie, Da;Jiang, Chuanwen;Zhang, Yanchi
    • Journal of Electrical Engineering and Technology
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    • v.9 no.1
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    • pp.80-89
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    • 2014
  • A stochastic optimal power flow (S-OPF) model considering uncertainties of load and wind power is developed based on chance constrained programming (CCP). The difficulties in solving the model are the nonlinearity and probabilistic constraints. In this paper, a limit relaxation approach and an iterative learning control (ILC) method are implemented to solve the S-OPF model indirectly. The limit relaxation approach narrows the solution space by introducing regulatory factors, according to the relationship between the constraint equations and the optimization variables. The regulatory factors are designed by ILC method to ensure the optimality of final solution under a predefined confidence level. The optimization algorithm for S-OPF is completed based on the combination of limit relaxation and ILC and tested on the IEEE 14-bus system.

A Modified Particle Swarm Optimization for Optimal Power Flow

  • Kim, Jong-Yul;Lee, Hwa-Seok;Park, June-Ho
    • Journal of Electrical Engineering and Technology
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    • v.2 no.4
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    • pp.413-419
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    • 2007
  • The optimal power flow (OPF) problem was introduced by Carpentier in 1962 as a network constrained economic dispatch problem. Since then, it has been intensively studied and widely used in power system operation and planning. In the past few decades, many stochastic optimization methods such as Genetic Algorithm (GA), Evolutionary Programming (EP), and Particle Swarm Optimization (PSO) have been applied to solve the OPF problem. In particular, PSO is a newly proposed population based stochastic optimization algorithm. The main idea behind it is based on the food-searching behavior of birds and fish. Compared with other stochastic optimization methods, PSO has comparable or even superior search performance for some hard optimization problems in real power systems. Nowadays, some modifications such as breeding and selection operators are considered to make the PSO superior and robust. In this paper, we propose the Modified PSO (MPSO), in which the mutation operator of GA is incorporated into the conventional PSO to improve the search performance. To verify the optimal solution searching ability, the proposed approach has been evaluated on an IEEE 3D-bus test system. The results showed that performance of the proposed approach is better than that of the standard PSO.