Transmission expansion planning using LP-based particle swarm optimization
05 Jun 2006-pp 207-212
TL;DR: An optimal expansion plan for the existing transmission network is proposed that formulates the design requirements of network planning as an operational planning model and is solved by an optimization algorithm such that an optimal planning scheme is obtained satisfying all constraints.
Abstract: This paper proposes a method of solving transmission network expansion problem using LP-Based particle swarm optimization. An optimal expansion plan for the existing transmission network is proposed. It formulates the design requirements of network planning as an operational planning model and is solved by an optimization algorithm such that an optimal planning scheme is obtained satisfying all constraints. Particle swarm optimization is a population-based search algorithm. The experimental results for linear programming technique, LP-based PSO and DC model are given.
Citations
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04 Oct 2012
TL;DR: This work proposes a parallel implementation of the Local Particle Swarm Optimization (LPSO) version to solve the Static Transmission Expansion Planning problem using the DC network model.
Abstract: It is well known that the Transmission Expansion Planning (TEP) is a formidable combinatorial problem; therefore more optimization techniques are needed to solve it in an efficient way. In this work it is proposed a parallel implementation of the Local Particle Swarm Optimization (LPSO) version to solve the Static Transmission Expansion Planning problem using the DC network model. The well-known Garver and IEEE 24-bus networks are used to present the results of this new approach.
23 citations
25 Jul 2010
TL;DR: Numerical results demonstrate that DMA has powerful computational capability and is capable of solving different dimensions of expansion planning problems efficiently with small population size.
Abstract: Monkey algorithm (MA) is one of the evolution algorithms originally developed for optimization problems with continuous variables. In this paper, a discrete monkey algorithm (DMA) was proposed for transmission network expansion planning, one discrete optimization problem. It includes the representation of solution, the modification of objective function, climb process, watch-jump process, cooperation process, somersault process, stochastic perturbation mechanism and termination criteria. Large-step and small-step climb process are designed to avoid the disordered climb direction during the MA optimization process. Cooperation process and stochastic perturbation mechanism are also introduced to improve computational efficiency. The proposed method is applied to a 18-bus system and the IEEE 24-bus system. Numerical results demonstrate that DMA has powerful computational capability and is capable of solving different dimensions of expansion planning problems efficiently with small population size.
22 citations
Cites background from "Transmission expansion planning usi..."
...Metaheuristic algorithms, such as genetic algorithm (GA)[1-2], tabu search (TS)[3-4], simulated annealing (SA)[5], greedy randomized adaptive search procedure (GRASP)[6] and particle swarm optimization (PSO)[7-8], are widely used due to the capability to derive optimal solutions irrespective of the continuity of objective functions and the complexity of search This work was supported by Key Projects in the National Science & Technology Pillar Program during the Eleventh Five-year Plan Period of China (2008BAA13B01)....
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...While for BPSO, with 300 particles, there are five trials that converge to local optima....
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...The optimal expansion scheme has the following topology: 6 10 1bus busx − = , 7 8 2bus busx − = , 10 12 1bus busx − = , 14 16 1bus busx − = , 16 17 1bus busx − = , 20 23 1bus busx − = BPSO are applied to this system....
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...It can be seen from Table II that when the dimension of the TNEP problem increases to 41, both IGA and BPSO need the enormous population, which leads to the large computation burden....
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...Primary Monkey Algorithm (PMA), Improved Genetic Algorithm (IGA) combining elitism and simulated annealing mutation [11] and Basic Particle Swarm Optimization (BPSO) are also applied to this system for comparison....
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01 Jan 2013
TL;DR: This chapter proposes a load shedding based TEP formulation using the DC and AC model, and four Particle Swarm Optimization (PSO) based algorithms applied to the TEP problem: Global PSO, Local PSO.
Abstract: The Transmission Expansion Planning (TEP) entails to determine all the changes needed in the electric transmission system infrastructure in order to allow the balance between the projected demand and the power supply, at minimum investment and operational costs. In some type of TEP studies, the DC model is used for the medium and long term time frame, while the AC model is used for the short term. This chapter proposes a load shedding based TEP formulation using the DC and AC model, and four Particle Swarm Optimization (PSO) based algorithms applied to the TEP problem: Global PSO, Local PSO, Evolutionary PSO, and Adaptive PSO. Comparisons among these PSO variants in terms of robustness, quality of the solution, and number of function evaluations are carried out. Tests, detailed analysis, guidelines, and particularities are shown in order to apply the PSO techniques for realistic systems. DOI: 10.4018/978-1-4666-2666-9.ch013
10 citations
Journal Article•
TL;DR: In this paper, the authors proposed the application of ant colony optimization (ACO) to solve a static transmission expansion planning problem based on a DC power flow model to minimize the investment cost of transmission lines added to an existing network in order to supply the forecasted load as economically as possible and subject to many system constraints.
Abstract: This paper proposes the application of ant colony optimization (ACO) to solve a static transmission expansion planning(STEP) problem based on a DC power flow model. The major objective is to minimize the investment cost of transmission linesadded to an existing network in order to supply the forecasted load as economically as possible and subject to many systemconstraints i.e. the power balance, the generation requirements, line connections and thermal limits. The Garver's six-buses system, isanalyzed to appraise the feasibility of the ACO. The experimental results obtained by ACO are compared to those obtained by theconventional approaches of the Genetic Algorithm (GA), and the Tabu Search (TS) algorithm. The results show that the ACOmethod outperforms other methods in convergence characteristic and computational efficiency.
10 citations
TL;DR: Comparisons reveal that the proposed IE-CDE is able to find better solutions than the other algorithms yielding lower cost of expansion for the electric grid and the claim for superiority of the proposed method over others is substantiated by statistical significance tests.
Abstract: Proper investments for expansion of generation, transmission and distribution systems in an electric grid is a very important issue that rely on optimal expansion planning of the grid resources. Investments on transmission network influence those in generation and distribution side which motivates a co-optimization of all these different resources of a grid. The co-optimization based Generation - Transmission Expansion planning is a large scale, constrained, hard bound optimization problem. This research article proposes an Information exchange based Clustered Differential Evolution algorithm (IE-CDE) for solving the problem of expansion planning of generation and transmission resources in an electric grid. The proposed algorithm is first tested extensively on the CEC 2017 constrained optimization benchmark problems and the results are compared with those obtained by state-of-the art algorithms to investigate the efficiency of the proposed algorithm in solving challenging constrained optimization problems. Then the proposed algorithm IE-CDE is used to solve the challenging Generation-Transmission expansion planning problem (GT) on a test system called Garver system. The implementation is also extended to incorporate the expansion planning of demand management resources along with generation and transmission resources (GTD) on the same test system mentioned before as well as an additional one called IEEE 24 bus system. The results obtained by proposed IE-CDE on the GT and GTD expansion planning problems are compared with state of the art algorithms in the literature and the comparison reveal that the proposed method is able to find better solutions than the other algorithms yielding lower cost of expansion for the electric grid. The claim for superiority of the proposed method over others is also substantiated by statistical significance tests on the obtained results.
8 citations
References
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06 Aug 2002
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Abstract: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described.
35,104 citations
"Transmission expansion planning usi..." refers methods in this paper
...The Linear Programming problem in section II is solved using the particle swarm optimization technique[ 10 ]....
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TL;DR: In this paper, a particle swarm optimization (PSO) method for solving the economic dispatch (ED) problem in power systems is proposed, and the experimental results show that the proposed PSO method was indeed capable of obtaining higher quality solutions efficiently in ED problems.
Abstract: This paper proposes a particle swarm optimization (PSO) method for solving the economic dispatch (ED) problem in power systems. Many nonlinear characteristics of the generator, such as ramp rate limits, prohibited operating zone, and nonsmooth cost functions are considered using the proposed method in practical generator operation. The feasibility of the proposed method is demonstrated for three different systems, and it is compared with the GA method in terms of the solution quality and computation efficiency. The experimental results show that the proposed PSO method was indeed capable of obtaining higher quality solutions efficiently in ED problems.
1,635 citations
"Transmission expansion planning usi..." refers methods in this paper
...From the above case, we can learn that there are two key steps when applying PSO to optimization problems: the representation of the solution and the fitness function [9]-[ 11 ]....
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TL;DR: In this paper, an evolutionary-based approach to solve the optimal power flow (OPF) problem is presented. And the proposed approach has been examined and tested on the standard IEEE 30bus test system with different objectives that reflect fuel cost minimization, voltage profile improvement, and voltage stability enhancement.
Abstract: This paper presents an efficient and reliable evolutionary-based approach to solve the optimal power flow (OPF) problem. The proposed approach employs particle swarm optimization (PSO) algorithm for optimal settings of OPF problem control variables. Incorporation of PSO as a derivative-free optimization technique in solving OPF problem significantly relieves the assumptions imposed on the optimized objective functions. The proposed approach has been examined and tested on the standard IEEE 30-bus test system with different objectives that reflect fuel cost minimization, voltage profile improvement, and voltage stability enhancement. The proposed approach results have been compared to those that reported in the literature recently. The results are promising and show the effectiveness and robustness of the proposed approach.
1,209 citations
"Transmission expansion planning usi..." refers background in this paper
...From the above case, we can learn that there are two key steps when applying PSO to optimization problems: the representation of the solution and the fitness function [9]-[11]....
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TL;DR: The use of linear programming for network analysis to determine where capacity shortages exist and, most importantly, where to add new circuits to relieve the shortages is presented.
Abstract: One aspect of long-range planning of electric power systems involves the exploration of various designs for the bulk power transmission network. The use of linear programming for network analysis to determine where capacity shortages exist and, most importantly, where to add new circuits to relieve the shortages is presented. The new method of network estimation produces a feasible transmission network with near-minimum circuit miles using as input any existing network plus a load and generation schedule. An example is used to present the two steps of the method: 1) linear flow estimation and 2) new circuit selection. The method has become a fundamental part of computer programs for transmission network synthesis.
771 citations
"Transmission expansion planning usi..." refers background in this paper
...Therefore, the problem of selecting effective lines by the overall network is formulated as the linear programming problem[1]-[3], [12]....
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Book•
01 Jan 1994
TL;DR: In this article, the authors provide a comprehensive approach to the planning and reliability calculations for the expansion of power generation systems, transmission networks and plant maintenance scheduling, which is particularly appropriate for the power utility structures being set up throughout western economies.
Abstract: This work provides a comprehensive approach to the planning and reliability calculations for the expansion of power generation systems, transmission networks and plant maintenance scheduling The mathematical and statistical theory required by the reader is introduced and explained by means of examples at the beginning of the text and particular emphasis is given to operational research and reliability calculations This should provide the reader with a means to become familiarized with the mathematical techniques before applications are introduced Detailed case studies based on practical power engineering examples are presented in each chapter The work should be of interest to both students and practising engineers The techniques discussed in the text are particularly appropriate for the power utility structures being set up throughout western economies
408 citations
"Transmission expansion planning usi..." refers background in this paper
...Therefore, the problem of selecting effective lines by the overall network is formulated as the linear programming problem[1]-[3], [ 12 ]....
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