A parallel genetic algorithm for generation expansion planning

Fukuyama, Yoshikazu; Chiang, Hsiao Dong

doi:10.1109/59.496180

Cited by 101 publications

(41 citation statements)

References 11 publications

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“…There have been only a few researches proposing GA for solving different models yet related to the PGEP problem. For example, Fukuyama and Chiang (1996) applied GA and Park et al (1999) applied an evolutionary programming algorithm for solving a simple PGEP problem with a good performance of solutions compared with those of the conventional method.…”

Section: Introductionmentioning

confidence: 98%

Power generation expansion planning with emission control: a nonlinear model and a GA-based heuristic approach

Sirikum

Techanitisawad

2006

Int. J. Energy Res.

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SUMMARYThis paper presents an application of genetic algorithms (GA) for solving the long-term power generation expansion planning (PGEP) problem, a highly constrained nonlinear discrete optimization problem. The problem is formulated into a mixed integer nonlinear programming (MINLP) program that determines the most economical investment plan for additional thermal power generating units over a planning horizon, subject to the requirements of power demands, power capacities, loss of load probability (LOLP) levels, locations, and environmental limitations. Computational results show that the GA-based heuristic method can solve the PGEP problem effectively and more efficiently at a significant saving in runtime, when compared with a commercial optimization package.

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Section: Introductionmentioning

confidence: 98%

Power generation expansion planning with emission control: a nonlinear model and a GA-based heuristic approach

Sirikum

Techanitisawad

2006

Int. J. Energy Res.

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“…To name a few, several artificial intelligence techniques have been applied to solve the problem, such as fuzzy theory [2,3], artificial neural network [4], genetic algorithm [5,6], simulated annealing [7], particle swarm optimization [8], etc. Also, in order to tackle the various uncertainties, stochastic programming [9], stochastic mixed-integer programming [10] and fuzzy-based mixed-integer programming [11], etc.…”

Section: Introductionmentioning

confidence: 99%

Integrated Optimization of Combined Generation and Transmission Expansion Planning Considering Bus Voltage Limits

Kim¹,

Kim²

2014

Journal of Electrical Engineering and Technology

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-A novel integrated optimization method is proposed to combine both generation and transmission line expansion problem considering bus voltage limit. Most of the existing researches on the combined generation and transmission expansion planning cannot consider bus voltages and reactive power flow limits because they are mostly based on the DC power flow model. In this paper the AC power flow model and nonlinear constraints related to reactive power are simplified and modified to improve the computation time and convergence. The proposed method has been successfully applied to Garver's six-bus system which is one of the most frequently used small scale sample systems to verify the transmission expansion method.

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“…Either GEP or TEP has been widely investigated in the past research. For GEP, different techniques have been used [11], for instance, fuzzy logic [12], genetic algorithm (GA) [13], particle swarm optimization (PSO) [14], Tabu search [15] and etc. However, without the geographical information of generators and transmissions, all generators were just considered to be at a single nodal point.…”

Section: Introductionmentioning

confidence: 99%

Integrated generation-transmission expansion planning for offshore oilfield power systems based on genetic Tabu hybrid algorithm

Sun

Xie

Wang

et al. 2016

J. Mod. Power Syst. Clean Energy

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To address the planning issue of offshore oilfield power systems, an integrated generation-transmission expansion planning model is proposed. The outage cost is considered and the genetic Tabu hybrid algorithm (GTHA) is developed to find the optimal solution. With the proposed integrated model, the planning of generators and transmission lines can be worked out simultaneously, which outweighs the disadvantages of separate planning, for instance, unable to consider the influence of power grid during the planning of generation, or insufficient to plan the transmission system without enough information of generation. The integrated planning model takes into account both the outage cost and the shipping cost, which makes the model more practical for offshore oilfield power systems. The planning problem formulated based on the proposed model is a mixed integer nonlinear programming problem of very high computational complexity, which is difficult to solve by regular mathematical methods. A comprehensive optimization method based on GTHA is also developed to search the best solution efficiently. Finally, a case study on the planning of a 50-bus offshore oilfield power system is conducted, and the obtained results fully demonstrate the effectiveness of the presented model and method.

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A parallel genetic algorithm for generation expansion planning

Cited by 101 publications

References 11 publications

Power generation expansion planning with emission control: a nonlinear model and a GA-based heuristic approach

Power generation expansion planning with emission control: a nonlinear model and a GA-based heuristic approach

Integrated Optimization of Combined Generation and Transmission Expansion Planning Considering Bus Voltage Limits

Integrated generation-transmission expansion planning for offshore oilfield power systems based on genetic Tabu hybrid algorithm

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