Total transfer capability calculations for competitive power networks using genetic algorithms
04 Apr 2000-pp 114-118
TL;DR: In this paper, the authors proposed a floating-point based genetic algorithm to solve the total transfer capability (TTC) problem, which is a nonlinear function of the system operating conditions and security constraints.
Abstract: The application of the genetic algorithms to solve the total transfer capability (TTC) problem is proposed in this paper. TTC is a nonlinear function of the system operating conditions and security constraints. The objective of the proposed genetic algorithm is to maximize a specific point-to-point power transaction without system constraint violation and to determine the TTC between the two points through global optimal search. The suggested genetic algorithm is simple to implement and can easily incorporate various constraints. The floating-point based genetic algorithm was tested on a 4 bus test system with good convergence. The test results are compared favorably with that obtained from the continuation power flow.
Citations
More filters
Book•
08 Oct 2008
TL;DR: Stochastic Security Analysis of Electrical Power Systems and Power System Transient Stability Analysis and Small-Signal Stability Analysis of Power Systems.
Abstract: Mathematical Model and Solution of Electric Network.- Load Flow Analysis.- Stochastic Security Analysis of Electrical Power Systems.- Power Flow Analysis in Market Environment.- HVDC and FACTS.- Mathematical Model of Synchronous Generator and Load.- Power System Transient Stability Analysis.- Small-Signal Stability Analysis of Power Systems.
248 citations
Additional excerpts
...(5) Genetic algorithm (GA) model [102]....
[...]
01 Jan 2005
TL;DR: An overview of differential evolution is provided and it is presented as an alternative to evolutionary algorithms with two application examples in power systems.
Abstract: As a relatively new population based optimization technique, differential evolution has been attracting increasing attention for a wide variety of engineering applications including power engineering Unlike the conventional evolutionary algorithms which depend on predefined probability distribution function for mutation process, differential evolution uses the differences of randomly sampled pairs of objective vectors for its mutation process Consequently, the object vectors' differences will pass the objective functions topographical information toward the optimization process, and therefore provide more efficient global optimization capability This paper aims at providing an overview of differential evolution and presenting it as an alternative to evolutionary algorithms with two application examples in power systems
86 citations
Cites methods from "Total transfer capability calculati..."
...Evolutionary algorithms have also been applied to solve the problem, [ 28 ]....
[...]
07 Aug 2002
TL;DR: In this paper, an algorithm of optimal power flow (OPF) to reduce the transaction curtailment through installing TCSC/SVC in the system is proposed in order to minimize the total amount of transactions being curtailed.
Abstract: One way to solve the congestion problem in a deregulated power system is re-dispatching the generation. This paper investigates the impacts of thyristor controlled series capacitor (TCSC) and static VAr compensator (SVC) on this re-dispatch method with the objective of minimizing the total amount of transactions being curtailed. An algorithm of optimal power flow (OPF) to reduce the transaction curtailment through installing TCSC/SVC in the system is proposed in this paper. Both the pool type transaction and the bilateral type transaction can be taken care using this method. This paper also investigates the improvement of total transfer capability (TTC) by using TCSC and SVC with the consideration of transaction patterns. With the increase of TTC, the possibility of congestion occurrence will be reduced. The TTC calculation is solved by OPF. Numerical examples are used to demonstrate the effect of TCSC/SVC on transaction curtailment and TTC improvements. The test results show that the effect is significant. Finally, this paper suggests some potential future research.
47 citations
Cites background from "Total transfer capability calculati..."
...Some assumptions are made for the formulation in this paper [ 14 ] ....
[...]
27 Feb 2006
TL;DR: An overview of differential evolution is provided and it is presented as an alternative to evolutionary algorithms with two application examples in power systems.
Abstract: As a relatively new population based optimization technique, differential evolution has been attracting increasing attention for a wide variety of engineering applications including power engineering. Unlike the conventional evolutionary algorithms which depend on predefined probability distribution function for mutation process, differential evolution uses the differences of randomly sampled pairs of objective vectors for its mutation process. Consequently, the object vectors' differences will pass the objective functions topographical information toward the optimization process, and therefore provide more efficient global optimization capability. This paper aims at providing an overview of differential evolution and presenting it as an alternative to evolutionary algorithms with two application examples in power systems
46 citations
TL;DR: In this article, a new probabilistic approach is developed to reduce the arbitrariness of the transmission system operator (TSO) to compute the transfer limits among areas in advance (weeks or months) with respect to the day-ahead market session.
Abstract: In a zonal market, the transmission system operator (TSO) has to compute the transfer limits among areas in advance (weeks or months) with respect to the day-ahead market session. The computation of such limits is usually made starting from some reference scenarios: this choice is arbitrary and has a strong influence on the results of the market. In this paper, a new probabilistic approach is developed to reduce such arbitrariness. A Monte Carlo method is applied to sample many different reference scenarios (in terms of generation patterns) to be adopted for the total transfer capacity (TTC) computation. Eventually, the probability density function of the TTC values is built. The proposed procedure allows the TSO to evaluate, for each possible choice of the TTC limit among areas, the maximum probability of congestion in a market framework, thus selecting the limit corresponding to the acceptable risk level. The new methodology is applied to the Italian system
33 citations
Cites methods from "Total transfer capability calculati..."
...In the technical literature, some methods are presented to evaluate the TTC given the starting scenario: the popular Continuation Power Flow algorithm [10], genetic algorithms [11], iterative linearization methods, and sensitivity calculations [12], [13]....
[...]
References
More filters
Book•
01 Jan 1997
TL;DR: Findings of Genetic Algorithms and Selected Topics in Engineering Design help solve problems in optimization, including Flow-Shop Sequencing Problems, Machine Scheduling Problems, and Facility Layout Design Problems.
Abstract: Foundations of Genetic Algorithms. Constrained Optimization Problems. Combinatorial Optimization Problems. Reliability Optimization Problems. Flow-Shop Sequencing Problems. Job-Shop Scheduling Problems. Machine Scheduling Problems. Transportation Problems. Facility Layout Design Problems. Selected Topics in Engineering Design. Bibliography. Index.
2,658 citations
TL;DR: In this paper, the authors present a method of finding a continuum of power flow solutions starting at some base load and leading to the steady-state voltage stability limit (critical point) of the system.
Abstract: The authors present a method of finding a continuum of power flow solutions starting at some base load and leading to the steady-state voltage stability limit (critical point) of the system. A salient feature of the so-called continuation power flow is that it remains well-conditioned at and around the critical point. As a consequence, divergence due to ill-conditioning is not encountered at the critical point, even when single-precision computation is used. Intermediate results of the process are used to develop a voltage stability index and identify areas of the system most prone to voltage collapse. Examples are given where the voltage stability of a system is analyzed using several different scenarios of load increase. >
1,666 citations
TL;DR: In this article, a program for available (transmission) transfer capability (ATC) calculations based on full AC power flow solution to incorporate the effects of reactive power flows, voltage limits and voltage collapse as well as the traditional line flow (thermal loading) effects is presented.
Abstract: This paper reports on the features and implementation of a program for available (transmission) transfer capability (ATC) calculations. A novel formulation of the ATC problem has been adopted based on full AC power flow solution to incorporate the effects of reactive power flows, voltage limits and voltage collapse as well as the traditional line flow (thermal loading) effects. An efficient continuation power flow approach with adaptive localization enhances speed in processing a large number of contingencies to determine ATC for each specified transfer. Test and performance results for practical power system models are presented.
329 citations
01 Jul 1994
TL;DR: In this article, two GA solutions to the economic dispatch problem are presented, which do not impose any convexity restrictions on the generator cost functions and can be coded to work on parallel machines.
Abstract: Two genetic algorithm (GA) solutions to the economic dispatch problem are presented. An advantage of the GA solutions is that they do not impose any convexity restrictions on the generator cost functions. Another advantage is that GAs can be very effectively coded to work on parallel machines. Test results with systems of up to 72 generating units with nonconvex cost functions show that both GAs outperform the dynamic programming solution to the economic dispatch problem. Furthermore, the execution time of the second GA solution increases almost linearly with the number of generators.
314 citations