Under a restructuring atmosphere, energy customers and producers generally create various bilateral real power markets. The transmission company usually carries out these bilateral power markets contained by the restr...
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Under a restructuring atmosphere, energy customers and producers generally create various bilateral real power markets. The transmission company usually carries out these bilateral power markets contained by the restrictions allowed through the power network design and working situation. The market operator can misuse the power markets and earn additional revenue. Flexible Alternate Current Transmission System devices are considered to be one such technology that helps in improving the bilateral real power markets. In this work, a new methodology for correct placement of Flexible Alternate Current Transmission System devices in the restructured power markets is proposed. A generalized evolutionary programming technique is projected to find out the optimal bilateral real power markets with and without Flexible Alternate Current Transmission System devices under a restructuring atmosphere. Flexible Alternate Current Transmission System devices, such as the Thyristor-Controlled Series Compensator, Static VAR Compensator, and Unified Power Flow Controller, are employed. Utilization of these devices helps in minimizing the cost and also helps in reducing the total overloads, excess power flow, and severity of overloading. A sample 4-bus system and 24-bus extra high-voltage Indian system are presented as examples to exemplify the use of this formulation to reduce the price of the bilateral power market.
The present work was conducted with the aim of finding a general method for solving the Unit Commitment (UC) problem. The proposed algorithm employs the evolutionary programming (EP) technique in which populations of ...
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The present work was conducted with the aim of finding a general method for solving the Unit Commitment (UC) problem. The proposed algorithm employs the evolutionary programming (EP) technique in which populations of contending solutions are evolved through random changes, competition, and selection. In the subject algorithm an overall UC schedule is coded as a string of symbols and viewed as a candidate for reproduction Initial populations of such candidates are randomly produced to form the basis of subsequent generations. The practical implementation of this procedure yielded satisfactory results when the EP-based algorithm was tested on a reported UC problem previously addressed by some existing techniques such as Lagrange Relaxation (LR), Dynamic programming (DP), and Genetic Algorithms (GAs). Numerical results for systems of up to 100 units are given and commented on.
Different mutation operators have been proposed in evolutionary programming, but for each operator there are some types of optimization problems that cannot be solved efficiently. A mixed strategy, integrating several...
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Different mutation operators have been proposed in evolutionary programming, but for each operator there are some types of optimization problems that cannot be solved efficiently. A mixed strategy, integrating several mutation operators into a single algorithm, can overcome this problem. Inspired by evolutionary game theory, this paper presents a mixed strategy evolutionary programming algorithm that employs the Gaussian, Cauchy, Levy, and single-point mutation operators. The novel algorithm is tested on a set of 22 benchmark problems. The results show that the mixed strategy performs equally well or better than the best of the four pure strategies does, for all of the benchmark problems. (c) 2006 Elsevier Inc. All rights reserved.
This paper develops an efficient and reliable evolutionary programming algorithm for solving the optimal power flow (OPF) problem. The class of curves used to describe generator performance does not limit the algorith...
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This paper develops an efficient and reliable evolutionary programming algorithm for solving the optimal power flow (OPF) problem. The class of curves used to describe generator performance does not limit the algorithm and the algorithm is also less sensitive to starting points. To improve the speed of convergence of the algorithm as well as its ability to handle larger systems, the algorithm is enhanced with gradient information. In the paper, the main elements of the evolutionary programming based OFF algorithm are presented. The algorithm is then demonstrated on the IEEE 30 bus test system.
\This paper studies evolutionary programming with mutations based on the Levy probability distribution. The Levy probability distribution has an infinite second moment and is, therefore, more likely to generate an off...
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\This paper studies evolutionary programming with mutations based on the Levy probability distribution. The Levy probability distribution has an infinite second moment and is, therefore, more likely to generate an offspring that is farther away from its parent than the commonly employed Gaussian mutation. Such likelihood depends on a parameter alpha in the Levy distribution. We propose an evolutionary,programming algorithm using adaptive as well as nonadaptive Levy mutations. The proposed algorithm was applied to multivariate functional optimization. Empirical evidence shows that, in the case of functions having many local optima, the performance of the proposed algorithm was better than that of classical evolutionary programming using Gaussian mutation.
This paper develops an evolutionary programming (EP) based algorithm for the combined heat and power dispatch problem for cogeneration systems. The problem is first formulated mathematically. The EP-based algorithm is...
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This paper develops an evolutionary programming (EP) based algorithm for the combined heat and power dispatch problem for cogeneration systems. The problem is first formulated mathematically. The EP-based algorithm is then described. In the algorithm, methods for satisfying the heat and power operation ranges of the units in the cogeneration systems and the demand constraints are presented. The new algorithm is applied to a test system containing two cogeneration units. The results are presented and the performance of the new algorithm assessed. (C) 2002 Published by Elsevier Science B.V.
Optimal power flow (OPF) has been widely used in power system operation and planning. In deregulated environment of power sector, it is of increasing importance, for determination of electricity prices and also for co...
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Optimal power flow (OPF) has been widely used in power system operation and planning. In deregulated environment of power sector, it is of increasing importance, for determination of electricity prices and also for congestion management. The classical methods are usually confirmed to specific cases of the OPF and do not offer great freedom in objective functions or the types of constraints that may be used. With a non-monotonic solution surface, classical methods are highly sensitive to starting points and frequently converge to local optimal solution or diverge altogether. This paper describes an efficient evolutionary programming based optimal power flow and compares its results with well known classical methods, in order to prove its validity for present deregulated power system analysis. (c) 2006 Elsevier Ltd. All rights reserved.
This paper presents an algorithm for solving security constrained optimal power flow problem through the application of evolutionary programming (EP). The controllable system quantities in the base-case state are opti...
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This paper presents an algorithm for solving security constrained optimal power flow problem through the application of evolutionary programming (EP). The controllable system quantities in the base-case state are optimised to minimize some defined objective function subject to the base-case operating constraints as well as the contingency-case security constraints. An IEEE 30-bus system is taken for investigation. The security constrained optimal power flow results obtained using EP are compared with those obtained using conventional security constrained optimal power flow. The investigations reveal that the proposed algorithm is relatively simple, reliable and efficient and suitable for on-line applications. (C) 2004 Elsevier B.V. All rights reserved.
The paper proposes an application of evolutionary programming (EP) to reactive power planning (RPP). RPP is a nonsmooth and nondifferentiable optimisation problem for a multiobjective function. Several techniques to m...
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The paper proposes an application of evolutionary programming (EP) to reactive power planning (RPP). RPP is a nonsmooth and nondifferentiable optimisation problem for a multiobjective function. Several techniques to make EP practicable have been developed. The proposed approach is demonstrated with the IEEE 30-bus system. The comprehensive simulation results show that EP is a suitable method to solve the RPP problem. A conventional optimisation method is used as the comparison method. The comparison shows that EP is better than the conventional method in the RPP problem.
In natural evolution, the individuals of species accumulate successive sight variations in their genes and the accumulated evolution information is inherited by their offspring. A new evolutionary programming algorith...
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In natural evolution, the individuals of species accumulate successive sight variations in their genes and the accumulated evolution information is inherited by their offspring. A new evolutionary programming algorithm adopting this concept is presented. Similar to natural evolution, the algorithm uses the accumulated evolution information inherited from its parent. The information is obtained through many generations of the evolution and speeds up the convergence of the evolutionary programming. The efficiency and robustness of the proposed algorithm has been verified through benchmark testing.
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